{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling PyPlot [d330b81b-6aea-500a-939a-2ce795aea3ee]\n",
      "└ @ Base loading.jl:1273\n",
      "┌ Info: Precompiling Optim [429524aa-4258-5aef-a3af-852621145aeb]\n",
      "└ @ Base loading.jl:1273\n",
      "┌ Info: Precompiling SetPyPlot [d6c70c59-9b85-50b1-926c-19fb5cf24e7d]\n",
      "└ @ Base loading.jl:1273\n",
      "┌ Warning: Package SetPyPlot does not have PyPlot in its dependencies:\n",
      "│ - If you have SetPyPlot checked out for development and have\n",
      "│   added PyPlot as a dependency but haven't updated your primary\n",
      "│   environment's manifest file, try `Pkg.resolve()`.\n",
      "│ - Otherwise you may need to report an issue with SetPyPlot\n",
      "└ Loading PyPlot into SetPyPlot from project dependency, future warnings for SetPyPlot are suppressed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Main.FractionalFlow"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the package\n",
    "include(\"../FractionalFlow/FractionalFlow.jl\")\n",
    "using PyPlot, SetPyPlot, Dierckx, Statistics, BlackBoxOptim\n",
    "import Calculus\n",
    "import GR\n",
    "FF = FractionalFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Water-flooding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gU4tTWXl8stU+1uapG5ubHFCkcjH1tdS4ceMYNmwYHTt2pEuXLsydO5fk5GRGjx4NwPDhw4mIiGDatGkADBgwgBkzZnDdddcVv5aaOHEit99+O56eWmZcRNxQahJ8+7y9fdOLEBYLXOi1KbLRsVEQXdVrI1KCqcXNXXfdxZkzZ5gyZQonTpygdevWrFixgkaNGgGQnJxcoqfmhRdewGKx8MILL3Ds2DHq1avHgAEDePnll836K4iIuE5hnn3ad9F5aHojdH4MgNTsPD7ecgSAJ29srrE2Ir9hMarZ+5ysrCwCAwPJzMykdu3aZscREbm8b8bDljlQqx6M3gAB9vGIL3+9m3kJh7guqg5LH+2q4kaqBUd+fmsErohIZbT3O3thAzBwdnFhk3Yunw83q9dG5PeouBERqWyyT8KyR+3tTo/CNb8udjov4SB5hTZiGwbS65p6JgUUqdxU3IiIVCY2G3w+GnLPQGgbuHly8UcZuQV8tEm9NiJXouJGRKQy2fw2HPwevGrYp317/boI6aJNR8gpsBLTIIAbYuqbGFKkclNxIyJSWRxPhNUXempunQb1WhR/lFtQxMILO38/2qupem1EfoeKGxGRyiD/HHw2EmyF0HIAdHiwxMeL/5PC2dxCooJr0q9NmDkZRaoIFTciIpXBt+Mh/QDUjrBvivk/PTOFVhvzfjgIwCM9muDlqW/dIr9H/0JERMy2aykkfgRYYMhcqBlc4uMvdxzneGYeIf6+3NGhoTkZRaoQFTciImbKSIavxtjbcX+Bxt1LfGyzGbyz/gAAI7tH4+etrWZErkTFjYiIWaxFsGQU5GdCwz9Ar+cuOWX1nlPsTz1HgJ8X93eOMiGkSNWj4kZExCwJr0PKZvAJgCHzwNO7xMeGYTB7nb3XZljnRgT4eZd2FxH5DRU3IiJmOLIJ1r9qb/f/GwRHX3LK5oPp7EjJwNfLgxHdLv1cREqn4kZEpKKdz4Clo8CwQdu7oe0fSz1tXoJ9htQfOzakXoBvqeeIyKVU3IiIVCTDgOVjIDMFgqKh3+ulnrbvVDZrk1KxWODh7k0qOKRI1eZVlpPeeusth288YsQIAgICHL5ORMSt7fgYfv4cPLxg6ALwLf375PwE+2rEt1zbgMYhtSoyoUiVV6biZsyYMTRs2BBPz7JNQUxJSaF///4qbkRE/lfafljxrL3dewI07FDqaanZeXyeeAyAUT001kbEUWUqbgC2bdtG/fpl26hNRY2IyG8UFcCSh6AwB6J7QLcxlz110cYjFFhttI+qQ4dGwZc9T0RKV6YxN5MmTcLf37/MN33++ecJDtY/SBGRYmunwIkfoUYwDH4XPEr/9ptbUMRHW44A9q0WRMRxZeq5mTRpkkM3jY+PL1cYERG3dGAtbPy7vT1wFtQOv+ypn20/SkZuIY3q1uTmaxtUUEAR93JVs6VeeeUVMjIynJVFRMT95KTB56Pt7Y4jIabfZU+12ozigcQju0fj6WG57LkicnlXVdxMnTqV9PR0Z2UREXEvhgHLHoNzp6BeDNzy8u+evvLnkySn51Knprc2yBS5CldV3BiG4awcIiLuZ+tc2PcdePrCHe+Bd43fPX3uhUX77u/UiJo+ZZ7vISK/oUX8RERc4eQuWDnR3u7zfxDa6ndP/2/yWRKTM/Dx9GB410YVEFDEfV3Vrwa7d+8mPPzyA+NERKqlglxYMhKs+XDNrXD9qCtesnDDYQAGxIZTP8DPxQFF3NtVFTeRkZHOyiEi4j5WToDTSeDfAAa+DZbfHxh8IvM8K346AcCIbo0rIKCIeyvTa6ng4GDS0tLKfNOoqCiOHDlS7lAiIlXWnuWw7T17e/AcqBVyxUsWbTqC1WbQKTqY1hGBLg4o4v7K1HOTkZHBN998Q2Bg2f7RnTlzBqvVelXBRESqnMxj8OWf7e2uT0LT3le85HyBlU+2JAPwUHdttSDiDGV+LfXAAw+4MoeISNVms8Lnf4LzZyGsHdwwsUyXLU08Sub5QqKCa3JTy1AXhxSpHspU3NhsNlfnEBGp2jbMhMMJ4F3LPu3by+eKl9hsBu/9275o34NdG2vRPhEn0VRwEZGrdXQbrL2wQN9t06Fu0zJdlrA/jQOnc/D39eKPHbVon4izqLgREbkaeVn2ad+GFVoNgXb3lvnSi702d3aMJMDP21UJRaodFTciIldjxdNw9jAERkH/v11x2vdF+1OzWb/3NBaL/ZWUiDiPihsRkfL6cTHsXAwWDxg6D2rUKfOl7288DMBNLUOJqlvTRQFFqicVNyIi5ZF+CL7+i73d8zmI6lzmSzPPF7Jk+zFAi/aJuILDxc19993H3Llz2bt3ryvyiIhUftZCWPIwFGRDVFfo8bRDl/9rWwrnC620CA2gS5O6LgopUn05XNz4+/szY8YMYmJiCA8P55577mHOnDkkJSW5Ip+ISOWzbhoc2wZ+gTBkLnh4lvlSm83gw832FdyHd22EpYxjdESk7Bwubt59912SkpI4fvw4M2bMIDAwkDfffJNWrVoRFhbmiowiIpXHoQRImGFvD3gL6ji2x966vakcOZNLbT8vBl8X4YKAIlLuMTcBAQEEBQURFBREnTp18PLyokGDBs7MJiJSueSmw9JHAAOuGwatBjl8i4u7f9/ZMZKaPle1d7GIXIbDxc348ePp3LkzISEhvPDCCxQUFBAfH8+pU6dITEx0RUYREfMZBnz5BGQfh7rNoO+rDt/iwOlzJOxLw2KB4V0aOz+jiAAO7C110fTp06lXrx6TJk1i4MCBtGzZ0hW5REQql+0LIWk5eHjD0AXgU8vhWyy6MP37xpj6mv4t4kIOFzeJiYmsX7+edevW8cYbb+Dp6UnPnj3p1asXvXr1UrEjIu4nNQm+fd7evulFCG/n8C2y8wr5bPtRAB7Qon0iLuVwcRMbG0tsbCxPPvkkAD/++CMzZ87kySefxGazYbVanR5SRMQ0hXn27RWKzkPTG6DzY+W6zWfbj5JTYKVpvVp0bxbi5JAi8r/KNZotMTGRdevWsW7dOhISEsjKyqJdu3b07t3b2flERMy1ehKc2gU1Q2DQHPBwfB6GzWawaJN9+veDXRtr+reIizlc3AQFBXHu3DliY2Pp1asXo0aNokePHtSuXdsV+UREzLP3O9gyx94e9A4EhJbrNgn70ziUZt/9e3B77f4t4moOFzcffvihihkRcX/ZJ2HZhVdQnR6Fa/qU+1YfbjoMwB0dGuLvq+nfIq7m8L+y/v37F7ePHj2KxWIhIkILUYmIG7HZYNmjkJsGoW3g5snlvlVKei5rklIBuL9zI2clFJHf4fDLY5vNxpQpUwgMDKRRo0ZERUVRp04dXnrpJWw2mysyiohUrM1vw4G14FUD7lgAXr7lvtVHW45gGNC9WQjN6vs7MaSIXI7DPTcTJkxgwYIFvPLKK3Tr1g3DMNiwYQMvvvgieXl5vPzyy67IKSJSMY7vgNUXempunQb1WpT7VnmFVv75nxQAhnVRr41IRXG4uPnggw+YP38+t99+e/Gx2NhYIiIieOyxx1TciEjVlX/OPu3bVggtB0CHB6/qdst3nuBsbiERdWpwY0x952QUkSty+LVUeno6MTExlxyPiYkhPT3dKaFEREzx7Xg4sx8Cwu2bYl7llO2LA4nv7RSFl2e5t/ITEQc5/K8tNjaWWbNmXXJ81qxZxMbGOiWUiEiF27UUEj8CLDBkLtQMvqrb7UjJ4Mejmfh4enD3HxzbOVxEro7Dr6Vee+01+vXrx+rVq+nSpQsWi4WNGzeSkpLCihUrXJFRRMS1MpLhqzH2dtxfIDruqm+5aNNhAPq3DaOuf/kHJIuI4xzuuenZsyd79+5l8ODBZGRkkJ6ezpAhQ/jll1+Ii7v6bwgiIhXKWgRLRkF+JkR0hF7PXfUt03MKWL7zBKCBxCJmKNdqUuHh4Ro4LCLuIeF1SNkMPgEwdD54el/1LRf/J4WCIhttGwbSLrKOE0KKiCPKVNzs3LmzzDds27ZtucOIiFSo5M2w/lV7u//fIDj6qm9ptRl8vMW+j9T9nRtpHykRE5SpuGnXrh0WiwXDMH73PIvFol3BRaRqOJ8BSx4GwwZt74a2f3TKbdf9ksrRs+epU9Ob22PDnXJPEXFMmcbcHDp0iIMHD3Lo0KHf/XPw4EGHA8yePZvo6Gj8/Pzo0KEDCQkJv3t+RkYGjz/+OGFhYfj5+dGyZUsNZBYRxxgGLB8DmSkQ1Bj6ve60W3+42d5rc2fHSPy8PZ12XxEpuzL13AwePJg1a9YQFBTElClTePrpp6lZs+ZVP3zx4sWMGTOG2bNn061bN95991369u3L7t27iYqKuuT8goICbr75ZurXr89nn31Gw4YNSUlJISAg4KqziEg1suNj+Plz8PCCoe+Br3O+hxw5k8P6vacBuK/Tpd/DRKRiWIwrvWsCatSowb59+2jYsCGenp6cOHGC+vWvfrXNTp060b59e955553iYy1btmTQoEFMmzbtkvPnzJnD9OnTSUpKwtu7bIP+8vPzyc/PL/46KyuLyMhIMjMztbO5SHWUth/e7QGFOXDjJIgb57RbT1uxh3d/OEjPa+rxwUPXO+2+ImL/+R0YGFimn99lHnMzYsQIunfvjmEYvP766/j7l74B3F//+tcyhSwoKGD79u0891zJaZd9+vRh48aNpV7z5Zdf0qVLFx5//HG++OIL6tWrx7333sv48ePx9Cy9+3fatGlMnlz+HX1FxI0UFcCSh+yFTeM46PaU026dV2hl8bYL+0hp928RU5WpuHn//feZNGkSy5cvx2Kx8M033+DldemlFoulzMVNWloaVquV0NDQEsdDQ0M5efJkqdccPHiQtWvXct9997FixQr27dvH448/TlFR0WWfGx8fz7hxv/5mdrHnRkSqobVT4MSPUCPIvgqxh/PGxCzfeYKMC/tI9dY+UiKmKlNx06JFCz799FMAPDw8WLNmjVNeSwGXTJM0DOOyUydtNhv169dn7ty5eHp60qFDB44fP8706dMvW9z4+vri66vVQUWqvQNrYePf7e2Bb0Nt585kujiQ+L7OUXh6aPq3iJkcXsTPZrM55cEhISF4enpe0kuTmpp6SW/ORWFhYXh7e5d4BdWyZUtOnjxJQUEBPj4+TskmIm4mJw0+H21vdxwJMf2cevudRzP4MSUDH08P7uyonmERs5m2Ta2Pjw8dOnRg1apVJY6vWrWKrl27lnpNt27d2L9/f4kCa+/evYSFhamwEZHSGQYsewzOnYJ6MdDn/5z+iI8u9Nrc1qYBIdpHSsR0phU3AOPGjWP+/Pm899577Nmzh7Fjx5KcnMzo0fbfsIYPH058fHzx+Y8++ihnzpzhqaeeYu/evXz99ddMnTqVxx9/3Ky/gohUdlvnwr7vwNMX7ngPfK5+GYv/lZlbyBc7jgPaR0qksijX3lLOctddd3HmzBmmTJnCiRMnaN26NStWrKBRI/s3iOTkZDw8fq2/IiMjWblyJWPHjqVt27ZERETw1FNPMX78eLP+CiJSmZ3cBSsn2tt9/g9CWzn9Ef/ankJ+kY2WYbVpHxXk9PuLiOPKtM6NO3FknryIVGEFuTCvN5xOgmtuhXs+BSfv82SzGdw4Yz2H0nKYOrgN92rhPhGXceTnt6mvpUREXGblC/bCxj/UPjvKBRtYbjiQxqG0HAJ8vRjYTvtIiVQWZXotFRQUVOadbdPT068qkIjIVduzHLYtsLcHz4FaIS55zIeb7AOJh3ZoSC1fU9/yi8j/KNO/xpkzZ7o6h4iIc2Qegy//bG93fRKa3uCSx5zIPM/qPacAuL+zXkeJVCZlKm4eeOABV+cQEbl6Nit8/ic4fxbC2sENE132qH9sScZmQJcmdWlWX5v3ilQmV9WPev78eQoLC0sc0yBdETHNhplwOAG8a8HQBeDlmvWvCops/OM/F/aR0vRvkUrH4QHFOTk5/PnPf6Z+/fr4+/sTFBRU4o+IiCmOboO1L9vbt70GIc1c9qiVu09yOjuf+gG+3Hxt6Suqi4h5HC5unn32WdauXcvs2bPx9fVl/vz5TJ48mfDwcBYtWuSKjCIivy8vC5aMBMMKrYZAu/tc+riLA4nvvj4Kb09NOhWpbBx+LfXVV1+xaJ2NK/0AACAASURBVNEievXqxUMPPURcXBzNmjWjUaNGfPzxx9x3n2u/qYiIXGLFM3D2MARGQf+/uWTa90X7TmWz5VA6nh4W7rle+0iJVEYO/8qRnp5OdHQ0YB9fc3Hqd/fu3fnhhx+cm05E5Ep2/hN2fgoWDxg6D2rUcenjLu4jdXPLUMICa7j0WSJSPg4XN02aNOHw4cMAXHvttfzzn/8E7D06deq49puKiEgJ6Ydg+Th7u+dzENXZpY/LyS9iyX+PAXB/Zw0kFqmsHC5uRowYwY8//ghAfHx88dibsWPH8swzzzg9oIhIqayFsORhKMiGqC4Q9xeXP/LzxGOcyy+iSb1adGtW1+XPE5HycXjMzdixY4vbvXv3JikpiW3bttG0aVNiY2OdGk5E5LLWTYNj28A3EIbMA0/XrhBsGEbxK6n7OzUq86rtIlLxrvq7QVRUFFFRWp1TRCrQoQRImGFv3/4m1HH9wN5tR86SdDIbP28PhnZo6PLniUj5lau4WbNmDWvWrCE1NRWbzVbis/fee88pwURESpWbDksfAQy4bhi0Glwhj704/XtQuwgCa3hXyDNFpHwcLm4mT57MlClT6NixI2FhYeqaFZGKYxjw5ROQfRzqNoO+r1bIY09n5/PNrhOABhKLVAUOFzdz5szh/fffZ9iwYa7IIyJyedsXQtJy8PC2b6/gU6tCHvvPbSkUWg2ui6pD64jACnmmiJSfw7OlCgoK6Nq1qyuyiIhcXmoSfPu8vX3TJAhvVyGPtdoMPr4wkHiYem1EqgSHi5uHH36YTz75xBVZRERKV5hn316h6Dw0vQE6P15hj16z5xTHM/MIruXDbW3CKuy5IlJ+Dr+WysvLY+7cuaxevZq2bdvi7V1yYN2MGTOcFk5EBIDVL8KpXVAzBAbNAY+K28/pwwu9Nnd2jMTP27PCnisi5edwcbNz507atbN3B+/atavEZxpcLCJOt3clbHnH3h70DgRU3C7ch9JySNiXhsUC93XSkhciVYXDxc3333/vihwiIpfKPgnLHrW3Oz0K1/Sp0MdfXLSvd4v6RAbXrNBni0j5XVXf7tGjRzl27JizsoiI/Mpmsxc2uWkQ2gZuerFCH59bUMQ/t6UAMKyLBhKLVCUOFzc2m40pU6YQGBhIo0aNiIqKok6dOrz00kuXLOgnIlJum9+GA2vBqwYMnQ/efhX6+C92HCc7r4hGdWvSs3m9Cn22iFwdh19LTZgwgQULFvDKK6/QrVs3DMNgw4YNvPjii+Tl5fHyyy+7IqeIVCfHd8Dqyfb2rVOhfkyFPt4wDBZt+nUfKQ8PjScUqUocLm4++OAD5s+fz+233158LDY2loiICB577DEVNyJydfLP2ad92wohpj90GFHhEbYfOcueE1n4ennwx47aR0qkqnH4tVR6ejoxMZf+FhUTE0N6erpTQolINfbtc3BmPwSEw+1/BxNmYV7stRnYLpw6NX0q/PkicnUcLm5iY2OZNWvWJcdnzZpFbGysU0KJSDX18+eQ+CFggSFzoWZwhUdIzc4r3kdqeJfGFf58Ebl6Dr+Weu211+jXrx+rV6+mS5cuWCwWNm7cSEpKCitWrHBFRhGpDjKS4cun7O24cRAdZ0qMT7dqHymRqs7hnpuePXuyd+9eBg8eTEZGBunp6QwZMoRffvmFuDhzvhmJSBVnLYKlj0B+JkR0hF7xpsQostr4ZEsyAMM1/VukynK45wYgPDxcA4dFxHkS3oDkTeATYJ/27el95WtcYNXuU5zMyqOu9pESqdLKVNzs3LmT1q1b4+Hhwc6dO3/33LZt2zolmIhUE8mbYf0r9nb/GRAcbVqUiwOJ774+El8v7SMlUlWVqbhp164dJ0+epH79+rRr1w6LxYJhGJecZ7FYsFqtTg8pIm7qfAYseRgMG7S9G9reaVqUX05ms+ngGTwscG8nvZISqcrKVNwcOnSIevXqFbdFRK6aYcDyMZCZAkGN4bbppsb5YNNhAPpc24CIOjVMzSIiV6dMxU2jRr/+FnPkyBG6du2Kl1fJS4uKiti4cWOJc0VELmvHx/ap3x5eMPQ98KttWpTM3EI+/699n7wHujY2LYeIOIfDs6V69+5d6mJ9mZmZ9O7d2ymhRMTNpe2HFc/a272fh4YdTI3zr+0pnC+00iI0gM5NKn5tHRFxLoeLG8MwsJSyYuiZM2eoVauWU0KJiBsrKrBvr1CYA43joNsYU+NYbb/uI/VA18alfn8TkaqlzFPBhwwZAtgHDT/44IP4+voWf2a1Wtm5cyddu3Z1fkIRcS9rX4ITO6BGkH0VYg9zZyWt35tKcnoutf28GHRduKlZRMQ5ylzcBAbaV+o0DIOAgABq1Ph1wJ2Pjw+dO3dm1KhRzk8oIu7jwFrY+Ja9PfBtqG1+MfH+RnuvzV1/iKSmT7mW/hKRSqbM/5IXLlwIQOPGjXnmmWeoWbOmy0KJiBvKSYPPR9vbHUdCTD9z8wAHTp/jh72nsVhgWOfGZscRESdxeMzN8OHDOXbs2CXH9+3bx+HDh52RSUTcjWHAssfg3CmoFwN9/s/sRAB8eGGszQ0t6hNVV7+wibgLh4ubBx98kI0bN15yfMuWLTz44IPOyCQi7mbrXNj3HXj6wtAF4GN+IXEuv4jPth8FNP1bxN04XNwkJibSrVu3S4537tyZHTt2OCWUiLiRk7tg5UR7u89L0KC1uXkuWLL9KOfyi2hSrxbdm4WYHUdEnMjh4sZisZCdnX3J8czMTG29ICIlFeTap31b86H5LXD9I2YnAsBmM3h/42EAHuzaGA8PTf8WcScOFzdxcXFMmzatRCFjtVqZNm0a3bt3d2o4EaniVr4Ap5PAPxQGzYZKsobMur2pHErLIcDPi6HtG5odR0SczOF5j6+99ho9evSgRYsWxMXFAZCQkEBWVhZr1651ekARqaL2LIdtC+ztwXOgVuV59bNww2EA7v5DJLV8Nf1bxN043HNz7bXXsnPnTu68805SU1PJzs5m+PDhJCUl0bp15XiXLiImyzoOX/7Z3u76BDS9wdw8/2PfqWwS9qXhYYHhXRqbHUdEXKBcv7KEh4czdepUZ2cREXdgs8LSR+D8WQiLhRv+anaiEhZeGGtz87WhRAabP2tLRJyv3P2xubm5JCcnU1BQUOJ427ZtrzqUiFRhG96EwwngXcu+27eXj9mJimXkFrD0v/bp3yO6RZucRkRcxeHi5vTp04wYMYJvvvmm1M81Y0qkGju6Hb5/2d6+7TUIaWZunt/4x9YU8gptXBtWm07R2v1bxF05POZmzJgxnD17ls2bN1OjRg2+/fZbPvjgA5o3b86XX37piowiUhXkZcGSh8BWBK2GQLv7zE5UQpHVxoebDgMwopt2/xZxZw733Kxdu5YvvviCP/zhD3h4eNCoUSNuvvlmateuzbRp0+jXz/z9YkTEBCuegbOHITAK+v+t0kz7vui7n09xPDOPurV8GBBr/oadIuI6Dvfc5OTkUL9+fQCCg4M5ffo0AG3atOG///2vc9OJSNWw85+w81OweMDQeVCjjtmJLrFwwyEA7usUhZ+3p8lpRMSVHC5uWrRowS+//AJAu3btePfddzl27Bhz5swhLCzM6QFFpJJLPwTLx9nbPcdDVGdz85QiMfks246cxdvTwv2dG5kdR0RczOHXUmPGjOHEiRMATJo0iVtuuYWPP/4YHx8f3n//fWfnE5HKzFoISx6GgmyI6gJxT5udqFQL/m3vtbk9NoL6tf1MTiMiruZwcXPffb8OErzuuus4fPgwSUlJREVFERJSeVYgFZEKsO4VOLYNfANhyFzwrHyr/R49m8s3u04CMLK7pn+LVAcOvZYqLCykSZMm7N69u/hYzZo1ad++vQobkermUAIkvGFvD5gJdaLMzXMZ7284jNVm0L1ZCNeG1zY7johUAIeKG29vb/Lz850+hXL27NlER0fj5+dHhw4dSEhIKNN1n376KRaLhUGDBjk1j4hcQW66fRViDLjufmg9xOxEpcrKK+TT/6QAMDJOvTYi1YXDA4qfeOIJXn31VYqKipwSYPHixYwZM4YJEyaQmJhIXFwcffv2JTk5+XevO3LkCE8//XTx5p0iUkEMA758ArKPQ91mcOurZie6rH/+J4Vz+UU0q+9Pz+b1zI4jIhXEYhiG4cgFgwcPZs2aNfj7+9OmTRtq1apV4vOlS5c6FKBTp060b9+ed955p/hYy5YtGTRoENOmTSv1GqvVSs+ePRkxYgQJCQlkZGSwbNmyMj0vKyuLwMBAMjMzqV1bXdQiDtu2EJaPAQ9veHg1hLczO1Gpiqw2ek5fx7GM87wypA13X185X5uJSNk48vPb4dF/derUYejQoeUO978KCgrYvn07zz33XInjffr0YePGjZe9bsqUKdSrV4+RI0de8RVWfn4++fn5xV9nZWVdXWiR6iw1Cb6Nt7dvmlRpCxuAb3ad5FjGeerW8mHQdRFmxxGRClSm4ubLL7+kb9++eHt7s3DhQqc9PC0tDavVSmhoaInjoaGhnDx5stRrNmzYwIIFC9ixY0eZnjFt2jQmT5581VlFqr3CPFgyEorOQ9MboPPjZie6LMMwmJ9wEID7OzfSon0i1UyZxtwMHjyYjIwMADw9PUlNTXVqiN8OUDYMo9RBy9nZ2dx///3MmzevzLOz4uPjyczMLP6TkpLilMwi1c7qF+HULqgZAoPmgIfDQ/YqzLYjZ/nxaCY+Xh4M66JF+0SqmzL13NSrV4/NmzczYMCAyxYe5RESEoKnp+clvTSpqamX9OYAHDhwgMOHDzNgwIDiYzabDQAvLy9++eUXmjZtWuIaX19ffH19nZJXpNrauxK2XBgXN+gdCLj032dlMvcHe6/N4HYRhPjr379IdVOm4mb06NEMHDgQi8WCxWKhQYMGlz3XarWW+eE+Pj506NCBVatWMXjw4OLjq1atYuDAgZecHxMTw08//VTi2AsvvEB2djZvvvkmkZGRZX62iJRR9ilY9qi93Wk0XNPH3DxXsD/1HKt2n8JigVE9mpgdR0RMUKbi5sUXX+Tuu+9m//793H777SxcuJA6dZyzMd64ceMYNmwYHTt2pEuXLsydO5fk5GRGjx4NwPDhw4mIiGDatGn4+fnRunXrEtdfzPHb4yLiBDYbLBsNuWkQ2hpuqvzj1+b+cACAm1uG0qy+v8lpRMQMZZ4tFRMTQ0xMDJMmTeKPf/wjNWvWdEqAu+66izNnzjBlyhROnDhB69atWbFiBY0a2d+TJycn41GJ3+2LuLXNs+HAWvCqAUMXgHfl3pfpVFYenyceA+BPPZte4WwRcVcOr3NT1WmdG5EyOr4D5t8EtkLo/zfo+JDZia5o2jd7eHf9Qf7QOIh/je5qdhwRcSJHfn6rS0RELlWQY5/2bSuEmP7QYYTZia4oK6+QTzbbVzb/Uw/12ohUZypuRORS34yHM/shIBxu/zs4eT85V/jHlmSy84toXt+fG2Lqmx1HREyk4kZESvr5c0j8ELDAkLlQM9jsRFeUX2TlvQ2HAHikRxM8PCp/MSYiruNQcVNYWEjv3r3Zu3evq/KIiJkykuHLp+ztuHEQXTU2pv1ix3FOZeUTWtuXge201YJIdedQcePt7c2uXbuctoifiFQi1iJY+gjkZ0JER+gVb3aiMrHZjOJF+0Z2j8bHSx3SItWdw98Fhg8fzoIFC1yRRUTMlPAGJG8CnwAYOh88vc1OVCYrd59if+o5Avy8uEc7f4sI5dgVvKCggPnz57Nq1So6duxIrVq1Snw+Y8YMp4UTkQqSvBnWv2Jv958BwdHm5ikjwzB4+/v9ADzQpTEBflWjIBMR13K4uNm1axft27cHuGTsjV5XiVRB5zNgySgwbND2Lmh7p9mJyuyHfWn8dCyTGt6ePNS9ahRkIuJ6Dhc333//vStyiIgZDAOWj4XMZAhqDLe9bnYih7y91t5rc2+nKIJr+ZicRkQqi3KPvNu/fz/fffcd58+fB+zdwyJSxez4BH5eCh5e9u0V/KrOqt1bD6Wz9XA6Pp4ejIrTBpki8iuHi5szZ85w4403cs0113Dbbbdx4sQJAB5++GH+8pe/OD2giLhI2n5Y8Yy93ft5aNjR3DwOujjWZmiHhjQIrNx7XolIxXK4uBk7dize3t4kJyeX2Dzzrrvu4ttvv3VqOBFxkaIC+/YKhTnQOA66jTE7kUN+OprJ+r2n8fSw8Kg2yBSR33B4zM3KlSv57rvvaNiwYYnjzZs358iRI04LJiIutPYlOLEDagTZVyH28DQ7kUMu9trcHhtOVN2aVzhbRKobh3tucnJySvTYXJSWloavr69TQomICx1YCxvfsrdvnwW1w83N46B9p7L59ueTADzaS702InIph4ubHj16sGjRouKvLRYLNpuN6dOn07t3b6eGExEny0mDz0fb2x0fgpb9zc1TDhd7bW5pFco1oQEmpxGRysjh11LTp0+nV69ebNu2jYKCAp599ll+/vln0tPT2bBhgysyiogzGAYsewzOnYJ6MdDnZbMTOWx/6jm+/PE4AH/u3dzkNCJSWTncc3Pttdeyc+dOrr/+em6++WZycnIYMmQIiYmJNG2qLmKRSmvrPNj3HXj62qd9+1S9sSqz1u7DZsBNLUNp0zDQ7DgiUkk53HOTnJxMZGQkkydPLvWzqCjt7SJS6ZzcBStfsLf7vAQNWpubpxz+t9dmzE3qtRGRy3O45yY6OprTp09fcvzMmTNER2v5c5FKp/C8fdq3NR+a3wLXP2J2onK52Gtz87WhtI5Qr42IXJ7DxY1hGKXuIXXu3Dn8/LSQlkil890EOJ0E/qEwaDZUwT3g/rfX5qkb1WsjIr+vzK+lxo0bB9hnR02cOLHEdHCr1cqWLVto166d8xOKSPklfQ3bFtjbg96BWiHm5imnv6vXRkQcUObiJjExEbD33Pz000/4+Py6SZ2Pjw+xsbE8/fTTzk8oIuWTdRy+eNze7voENLvR3DzlpF4bEXFUmYubi7uBjxgxgjfffJPatavOBnsi1Y7NCksfgfNnISwWbvir2YnK7e9r92EY0Ee9NiJSRg7Pllq4cKErcoiIM214Ew4ngHctGPoeePlc+ZpKaN+p7OJemyfVayMiZeRwcQPwn//8h3/9618kJydTUFBQ4rOlS5c6JZiIlNPR7fD9hQX6bnsNQpqZm+cqvLFyL4ZhX41YvTYiUlYOz5b69NNP6datG7t37+bzzz+nsLCQ3bt3s3btWgID9c1HxFT52bDkIbAVQavB0O4+sxOV248pGXz780k8LPB0nxZmxxGRKsTh4mbq1Kn87W9/Y/ny5fj4+PDmm2+yZ88e7rzzTi3gJ2K2r5+Gs4chMBL6z6yS074ven3lLwAMvq4hzbWHlIg4wOHi5sCBA/Tr1w8AX19fcnJysFgsjB07lrlz5zo9oIiU0c5/ws5PweIBQ+dDjTpmJyq3jQfSSNiXhrenRasRi4jDHC5ugoODyc7OBiAiIoJdu3YBkJGRQW5urnPTiUjZpB+C5fa1qOg5HqI6m5vnKhiGwfTv7L02914fRWRw1dsDS0TM5fCA4ri4OFatWkWbNm248847eeqpp1i7di2rVq3ixhur5joaIlWatRCWPAwF2RDZGeKq9npTq/ekkpicgZ+3B4/fUHUHQ4uIeRwubmbNmkVeXh4A8fHxeHt78+9//5shQ4YwceJEpwcUkStY9woc2wa+gTB0HniWaxJkpWCzGbx+oddmRLdo6gdoSxcRcZzFMAzD7BAVKSsri8DAQDIzM7UQoVR9hxLggwGAAXcshNZDzE50VZYlHmPM4h0E+Hnx72dvILCmt9mRRKSScOTnt8Njbu677z7mzZvH3r17yx1QRJwgN92+CjEGXHd/lS9s8ousxTOkRvdsqsJGRMrN4eLG39+fN954g5iYGMLDw7nnnnuYM2cOSUlJrsgnIqUxDPjyCcg+DnWbwa2vmp3oqi3aeISjZ88TWtuXEd0amx1HRKowh4ubd999l6SkJI4fP86MGTMIDAzkzTffpFWrVoSFhbkio4j81vb3IWk5eHjD0AXg6292oquSkVvA39fuA+AvN7egpk/VHTckIuZzuLi5KCAggKCgIIKCgqhTpw5eXl40aNDAmdlEpDSpSfBtvL190yQIb2duHif4+9r9ZOUVEdMggKEdGpodR0SqOIeLm/Hjx9O5c2dCQkJ44YUXKCgoID4+nlOnTpGYmOiKjCJyUWGefdp30XloegN0ftzsRFftyJkcFm06DMDzt7XE06PqrqosIpWDw32/06dPp169ekyaNImBAwfSsmVLV+QSkdKsfhFO/QQ1Q2DQHPAod+drpfHat79QaDXocU09elxTz+w4IuIGHC5uEhMTWb9+PevWreONN97A09OTnj170qtXL3r16qViR8RV9q6ELe/Y24NmQ0CouXmcYPuRs3z90wksFojvG2N2HBFxEw4XN7GxscTGxvLkk08C8OOPPzJz5kyefPJJbDYbVqvV6SFFqr3sU7DsUXu702i45hZz8ziBYRhMXbEHgD92aEjLMK07JSLOUa4pCYmJiaxbt45169aRkJBAVlYW7dq1o3fv3s7OJyI2GywbDblpENoabppsdiKn+PqnE2w/cpYa3p6Mu7mF2XFExI04XNwEBQVx7tw5YmNj6dWrF6NGjaJHjx5a7VfEVTbPhgNrwcvPPu3bu+pvSZBbUMTUr+29No/0aEKDwKr/dxKRysPh4ubDDz9UMSNSUY7vsA8iBrhlKtR3j3Epc9Yd4HhmHhF1avBor6ZmxxERN+NwcdO/f39X5BCR3yrIgSUjwVYIMf2h40NmJ3KKlPRc5vxwEIAX+rXEz9vT5EQi4m6q/jxSEXf1zXg4sx8CwuH2v4PFPdZ/+b+vd1NQZKNr07rc2loLf4qI86m4EamMfv4cEj8ELDDkXagZbHYip/j3vjS++/kUnh4WJg1ohcVNCjYRqVxU3IhUNhkp8NVT9nbcOIjuYW4eJym02pj81c8ADOvciBYNAkxOJCLuSsWNSGVis8LSUZCXCREdoVe82Ymc5sNNR9iXeo7gWj6Mvekas+OIiBtTcSNSmfzwOiRvAp8AGDofPL3NTuQUp7LymLFqLwDP3NKCwJru8fcSkcpJxY1IZZG8Gda/Ym/3ewOCo83N40STv/qZc/lFtIusw10dI82OIyJuTsWNSGVwPgOWjALDBm3vgti7zE7kNGuTTrHip5N4eliYOrgNHtr1W0RcTMWNiNkMA5aPhcxkCGoMt71udiKnyS0oYuIy+yDikd2juTZci3+KiOupuBEx245P4OelYPG0b6/g5z4FwJtr9nEs4zwRdWow5qbmZscRkWpCxY2ImdL2w4pn7O3ez0PDjubmcaKkk1ksSDgEwJSBrajpU659ekVEHKbiRsQsRQX27RUKc6BxHHQfa3Yip7HZDOKX/kSRzeDWVg24sWWo2ZFEpBpRcSNilrUvwYkdUCMIBr8LHu6zx9JHW46QmJyBv68XL97eyuw4IlLNVIriZvbs2URHR+Pn50eHDh1ISEi47Lnz5s0jLi6OoKAggoKCuOmmm9i6dWsFphVxggPfw8a37O3bZ0FghLl5nCglPZdXvkkC7GvaNAj0MzmRiFQ3phc3ixcvZsyYMUyYMIHExETi4uLo27cvycnJpZ6/bt067rnnHr7//ns2bdpEVFQUffr04dixYxWcXKScctLg8z/Z2x0fgpb9zc3jRDabwbOf7SS3wMr10cEM69zI7EgiUg1ZDMMwzAzQqVMn2rdvzzvvvFN8rGXLlgwaNIhp06Zd8Xqr1UpQUBCzZs1i+PDhVzw/KyuLwMBAMjMzqV3bfWalSBVhGPCPu2Hvt1AvBkZ9Dz41zU7lNB9uPsLEZbvw8/bguzE9aFS3ltmRRMRNOPLz29Sem4KCArZv306fPn1KHO/Tpw8bN24s0z1yc3MpLCwkOLj0XZPz8/PJysoq8UfENFvn2QsbT1/7tG83KmxS0nOZtmIPAONvjVFhIyKmMbW4SUtLw2q1EhpaciZFaGgoJ0+eLNM9nnvuOSIiIrjppptK/XzatGkEBgYW/4mM1NLvYpKTu2DlC/b2zVOgQWtz8ziRYRiMX3LhdVTjYB7o0tjsSCJSjZk+5gbAYim5HLthGJccK81rr73GP/7xD5YuXYqfX+mDFuPj48nMzCz+k5KS4pTMIg4pPG+f9m3Nh+Z9oNOfzE7kVJ9sTWbjgTP4eXvw6h1ttcWCiJjK1FW1QkJC8PT0vKSXJjU19ZLenN96/fXXmTp1KqtXr6Zt27aXPc/X1xdfX1+n5BUpt+8mwOkkqFUfBs6GMhTvVcXhtBxe/tr+OuqZW2KIDtHrKBExl6k9Nz4+PnTo0IFVq1aVOL5q1Sq6du162eumT5/OSy+9xLfffkvHju6zoqu4qaSvYdsCe3vwHPCvZ24eJyq02nhq8Q5yC6x0ig7mwa6NzY4kImJuzw3AuHHjGDZsGB07dqRLly7MnTuX5ORkRo8eDcDw4cOJiIgonjn12muvMXHiRD755BMaN25c3Ovj7++Pv7+/aX8PkVJlHYcvHre3uz4BzW40N4+TvbVmHz+mZFDbz4u/3dUOT72OEpFKwPTi5q677uLMmTNMmTKFEydO0Lp1a1asWEGjRvb1MZKTk/Hw+LWDafbs2RQUFHDHHXeUuM+kSZN48cUXKzK6yO+zWWHpI3D+LITFwg1/NTuRU209lM7b3+8HYOqQNoTXqWFyIhERO9PXualoWudGKkzCDFgzGbxrwp8SIKSZ2YmcJvN8Ibe9mcCxjPMMbd+QN+6MNTuSiLi5KrPOjYjbOrodvn/Z3u77mlsVNgB//WIXxzLOExVck8kDtXeUiFQuKm5EnC0/2z7t21YErQbDdfebnciplmw/yhc7juPpYWHm3e3w9zX97baISAkqbkSc7eun4ewhCIyE/jPdatp30sksJiz7CYCnHHF8nQAAIABJREFUbmxO+6ggkxOJiFxKxY2IM+38J+z8FCweMGQe1KhjdiKnOZdfxGMf/Ze8QhtxzUN4vLd7vWoTEfeh4kbEWdIPwfJx9naPZ6FRF3PzONHF7RUOpuUQFujHTE37FpFKTMWNiDNYC2HJw1CQDZGdocczZidyqkWbjvD1zhN4eViYdW976vpr1W/5//buOz6qKv//+GsyaaQQegJJIBB6E0gUqSGiguxGigr8FkFY1xXF3wZ0QVyMUqQpKq5fQUUEv6tL1qVYUcqSQGiKSGxBehEJTYEUIMkk5/tHZNaBgJmQzJDJ+/l4zIM7Z8699z0ngflw59x7Ra5fKm5EykPqLPjxC/ALgbsWgNVzJtnuOHyaZz7OAOBv/VoR00jzbETk+qbiRuRaHdwIac8XLyfMhRoN3ZunHJ3MzmPMO19SUGjo1y6MUd2i3B1JROQ3qbgRuRbnfi6+CjGm+JTvtoPcnajc5NkKGf32do6evUCTOoHMvqs9Fg8680tEPJeKG5GyMgY+/Atk/Qi1m0Lf2e5OVG6MMTy54lu2HzpNsL83C+6LJdjfx92xRERKRcWNSFltXww7PwQvH7jrDfDznBu3Ltx4gH9vP4KXBV75Qyei63rOexMRz6fiRqQsTu6CT58oXu79FDTo6N485Wj97pPMWLkTgEm/a03P5nXdnEhExDkqbkScVXABlt4PtvPQJB66POLuROVm74kcHvnnlxQZGBwbwR81gVhEKiEVNyLOWjsZjn8DAbVh4Kvg5Rl/jU5kXWDkos/JvmDjxqiaTBvQVhOIRaRS8ox/lUVcZfdq+Gx+8fKA+RAc5t485ST7QgEjF23jyOnzRNUO4NV7Y/Dztro7lohImai4ESmt7OPw3kPFyzc9CM37uDdPOcm3FfHQ21+SkZlFnSBf/vePnXUFYhGp1FTciJRGURG8NxrOnYJ6beC2qe5OVC6KigwTln7Fxr2nCPC1smjkTTSsHeDuWCIi10TFjUhpbJ0H+9aBtz/c/Sb4+Ls7UbmY/en3vJd+FG8vC/PvjaFdRIi7I4mIXDMVNyK/JfOr4knEAH1mQL2Wbo1TXl7+zx5e27AfgNl3tSdOp3yLiIdQcSNyNfm5xad9FxVAy99D7B/dnahcvL5hH8+v2Q3ApH6tuCsmws2JRETKj4obkav5dCL8tAeCG8CdL4MHnBq9eNMBZqz8HoC/3t6cB3o2cXMiEZHypeJG5Eq+ew++/F/AAoNeg4Ba7k50zf752WEmf5gBwP+/pSmP3NLMzYlERMqfihuRkpz5ofimmADdx0Hjnu7NUw7e3fYDk977BoA/92zCo7c1d3MiEZGK4e3uACLXnaJCWP4AXDgL4TEQ/zd3J7pmizYdYMovR2xGdo3iiTta6urDIuKxVNyIXGrDHDi8BXyDi+/2bfVxd6IyM8bwSspe5qwunjz8x26NSfp9KxU2IuLRVNyI/NrhrbB+VvHy756HWpV3sq0xhlmffG8/3TuxdzPG3tpMhY2IeDwVNyIXnT8Dyx4AUwTtBsMNQ9ydqMwKiwxPvf8t73x2GCg+3VtnRYlIVaHiRgTAGPhoHJw9DDWjio/aVFLn8wtJTN7B6ozjWCwwfUA7/tC5obtjiYi4jIobEYD0f8J3y8FihbsWgn91dycqk5PZefzprW18deQsvt5evDD4Bn7fvoG7Y4mIuJSKG5Gf9sHK8cXL8X+DiFj35imjPcezGbV4G0dOn6dmgA8LRsQSG1X5r80jIuIsFTdStdnyYekfoSAXonoUX9OmEtq09xSj395O9gUbjesE8ubIG2lcJ9DdsURE3ELFjVRt66ZBZjpUqwkDXwMvq7sTOcUYw4K0/cz65HuKDNwYVZPXh8dSM9DX3dFERNxGxY1UXftSYPPfi5fvfBlCwt2bx0m5eTYmLP2aj7/JBOCuThFMH9gWf5/KVaCJiJQ3FTdSNeWeghUPFi/HjIJWCe7N46T9J3N48B/b2XMiBx+rhacS2nBv54a6ho2ICCpupCoyBt4fAznHoU4L6DPD3Ymc8sFXR5m0/Buy82zUC/Zj/r2diGmkicMiIhepuJGq5/MFsPtTsPrC3QvBN8DdiUolJ8/GU+9/y/IvfwTgpqha/M+wjtQL9ndzMhGR64uKG6lajn8Hq58sXr5tGoS1c2+eUtpx+DSJyekc/vkcXhZ45JZm/OWWpnhbvdwdTUTkuqPiRqqOgvOw9H4ozINmt0PnB92d6Dfl24qYn7qPv6/bQ2GRIbxGNeYO7cCNun6NiMgVqbiRqmP1k3ByJwTWg/7z4DqffPvVD2d4fNnXfH8sG4A7b2jAtAFtCalWee9SLiLiCipupGr4/mPY9kbx8sBXIaiue/Ncxfn8Ql5Ys4uFGw9QZKBWoC9PJ7Tmzhsa6GwoEZFSUHEjni/raPHZUQBdHoGmvd2b5ypSdp1g8gffceincwD079CAp37fmtpBfm5OJiJSeai4Ec9WVFh8PZvzp6H+DdD7aXcnKtGBU7lM+yiDdd+fAKB+iD/TB7bllpahbk4mIlL5qLgRz7bpJTiwAXwCiu/27X193ZYg+0IB/7NuL29uOkBBocHby8KoblH8pXczgv01t0ZEpCxU3IjnOrIdUqYXL9/xLNRp5t48v5JnK+Sfnx3mlZS9nMrJB6BXi7ok/b410XWD3JxORKRyU3EjnikvG5bdD0U2aD0AOt7r7kQA2AqLWPblEV5au4ejZy8A0LhOIEm/b6WvoEREyomKG/FMK8fD6QMQEgkJc91+2ndBYREfpB/llZS97D+VC0BYdX/+0rsZ98RG4KOL8YmIlBsVN+J5vv43fLUELF4waAFUq+m2KOfybSR//gNvpO23H6mpFejLw72iuffmRrqDt4hIBVBxI57l5wPw0bji5Z4ToFEXt8Q4dvYC//zsEP+79RBnzhUAUCfIjz92j2JElyiC/PRXT0SkouhfWPEchQWw/AHIz4bIm6HneJfu3hjDlv0/8fbWQ6z67jiFRQaARrUDeLBnNIM6hetIjYiIC6i4Ec+xfjYc2QZ+IXDXArC65tf7RPYFPkg/SvK2H9h7IsfeflPjWtzXJYq+bcOweunKwiIirqLiRjzDwY2wYU7xcsJcqNGwQnd3Pr+Q1RnHWP7lj6TtOckvB2kI9LUysFM4997ciJZh1Ss0g4iIlEzFjVR+536G5X8GDHS4F9oOqpDd5ObZSNl1gk+/PUbK9yfIzS+0v9apYQ0GdYqgf4cGuvieiIibqbiRys0Y+PAvkPUj1IqGO2aX6+Yzz55nw+6TrMk4wYY9J8m3Fdlfi6hZjUGdIhjYMZzGdQLLdb8iIlJ2Km6kctu+GHZ+CF4+cPdC8Lu2q/uey7ex/dBpNuw+yYbdp9h1PNvh9ajaAfRtW5++bcNoHx6Cl+bSiIhcd1TcSOVSWAhpaZCZCdVtsOOJ4vbeT0GDjk5v7sy5fL48fJrPDvzM5wd+5psjZ7FdnEADeFnghsga9Gpejz5tQ2kRGozFzRcEFBGRq1NxI5XH8uWQmAhHjoAV+FMghFkhqA10eeQ3Vz97voBdx7L5+sgZvjpylq+PnOHQT+cu69cgxJ9uTesQ16Iu3ZvWoUbA9XWzTRERubrroriZN28ezz33HJmZmbRp04a5c+fSo0ePK/ZftmwZSUlJ7Nu3j+joaKZPn87AgQNdmFhcbvlyuPvu4jk2ALf6FRc2uUXwwmfQ5D0YNAhjDD/l5nPwVC4HTuWy92QOu45ls+tYNpm/XCH4Uo3rBHJTVC1ualz8iKwV4MI3JiIi5c3txc2//vUvxo4dy7x58+jWrRuvvfYad9xxBxkZGTRsePnpvFu2bGHIkCFMmzaNgQMHsmLFCgYPHszGjRvp3LmzG96BVLjCwuIjNhcLm6becLNf8fL7FzDZhtMPPMyoH2qz/+cLZOfZrripBiH+tA0PoX1ECO0jatA+IkRHZkREPIzFGGN+u1vF6dy5M506dWL+/Pn2tlatWjFgwABmzpx5Wf8hQ4aQlZXFJ598Ym/r27cvNWvWZMmSJb+5v6ysLEJCQjh79izVq5ffdUjybIWczM4rt+15CmOgyBgKi8wvfxbfRDLPVkS+rYg8WyF5tiLO5dvIzSvkXL6NnLxCss4XcPaXR3j6Vqa99JfiDQZaYHQgBHnBZ3nw6X/HfOj/m8HWhu2xWKBBSDUa1wmkSd1AWoQF0yI0mOZhwVTXadoiIpWSM5/fbj1yk5+fz/bt25k4caJD++23387mzZtLXGfLli2MGzfOoa1Pnz7MnTu3xP55eXnk5f33AzArK+saU5fsu6NZDJpXcma5Nnfu/+G/T7yBswZyC2GNYzH5t4418B/Rk4a1AnSbAxGRKsytxc2pU6coLCwkNDTUoT00NJRjx46VuM6xY8ec6j9z5kymTJlSPoGvwgL4eXtV+H4qG4sFrBYLXl4WrF4WvCwWfK1e+Hr/8rB64efjRZCfNwG+VgJ/+bO6vw81AnwIqeZD4xZ58OEvGzxr4M3c4iM4hY77an9jKwgNdvl7FBGR64vb59wAl51aa4y56um2zvR/4oknePTRR+3Ps7KyiIyMvIa0JevYsCa7nrmj3LcrQKdweCoCfvzxl++5gOxffZtqsUBEBFxlErqIiFQdbj3UUKdOHaxW62VHXU6cOHHZ0ZmLwsLCnOrv5+dH9erVHR5SyVit8NJLxcuXFrEXn8+dW9xPRESqPLcWN76+vsTExLBmzRqH9jVr1tC1a9cS1+nSpctl/VevXn3F/uIhBg2CpUshPNyxPSKiuH1QxdxPSkREKh+3fy316KOPMnz4cGJjY+nSpQuvv/46hw8fZvTo0QCMGDGC8PBw+5lTiYmJ9OzZk9mzZ9O/f3/ef/991q5dy8aNG935NsQVBg2C/v3/e4Xi+vWLv4rSERsREfkVtxc3Q4YM4aeffmLq1KlkZmbStm1bVq5cSaNGjQA4fPgwXl7/PcDUtWtXkpOTefLJJ0lKSiI6Opp//etfusZNVWG1Qq9e7k4hIiLXMbdf58bVKuo6NyIiIlJxnPn81rnLIiIi4lFU3IiIiIhHUXEjIiIiHkXFjYiIiHgUFTciIiLiUVTciIiIiEdRcSMiIiIeRcWNiIiIeBQVNyIiIuJR3H77BVe7eEHmrKwsNycRERGR0rr4uV2aGytUueImOzsbgMjISDcnEREREWdlZ2cTEhJy1T5V7t5SRUVFHD16lODgYCwWS7luOysri8jISH744Qfdt6oCaZxdQ+PsGhpn19FYu0ZFjbMxhuzsbBo0aOBwQ+2SVLkjN15eXkRERFToPqpXr66/OC6gcXYNjbNraJxdR2PtGhUxzr91xOYiTSgWERERj6LiRkRERDyKdfLkyZPdHcKTWK1WevXqhbd3lfvGz6U0zq6hcXYNjbPraKxdw93jXOUmFIuIiIhn09dSIiIi4lFU3IiIiIhHUXEjIiIiHkXFjYiIiHgUFTdOmjdvHo0bN8bf35+YmBjS0tKu2n/ZsmW0bt0aPz8/WrduzYoVK1yUtHJzZpwXLFhAjx49qFmzJjVr1uTWW2/l888/d2HaysvZ3+eLkpOTsVgsDBgwoIITegZnx/nMmTOMGTOG+vXr4+/vT6tWrVi5cqWL0lZezo7z3LlzadGiBdWqVSMyMpJx48Zx4cIFF6WtnDZs2EBCQgINGjTAYrHw3nvv/eY669evJyYmBn9/f5o0acKrr75a8UGNlFpycrLx8fExCxYsMBkZGSYxMdEEBgaaQ4cOldh/8+bNxmq1mhkzZpidO3eaGTNmGG9vb7N161YXJ69cnB3nP/zhD+aVV14xO3bsMDt37jSjRo0yISEh5siRIy5OXrk4O84XHTx40ISHh5sePXqY/v37uyht5eXsOOfl5ZnY2FjTr18/s3HjRnPw4EGTlpZm0tPTXZy8cnF2nN9++23j5+dn3nnnHXPgwAGzatUqU79+fTN27FgXJ69cVq5caSZNmmSWLVtmALNixYqr9t+/f78JCAgwiYmJJiMjwyxYsMD4+PiYpUuXVmhOFTdOuOmmm8zo0aMd2lq2bGkmTpxYYv/Bgwebvn37OrT16dPHDB06tMIyegJnx/lSNpvNBAcHm7feeqsi4nmMsoyzzWYz3bp1M2+88Ya57777VNyUgrPjPH/+fNOkSROTn5/vingew9lxHjNmjLnlllsc2h599FHTvXv3CsvoaUpT3EyYMMG0bNnSoe3BBx80N998c0VGM/paqpTy8/PZvn07t99+u0P77bffzubNm0tcZ8uWLZf179OnzxX7S9nG+VLnzp2joKCAWrVqVUREj1DWcZ46dSp169bl/vvvr+iIHqEs4/zBBx/QpUsXxowZQ2hoKG3btmXGjBkUFha6InKlVJZx7t69O9u3b7d/hb1//35WrlzJ7373uwrPW5Vc6XPwiy++oKCgoML2q0s0ltKpU6coLCwkNDTUoT00NJRjx46VuM6xY8ec6i9lG+dLTZw4kfDwcG699daKiOgRyjLOmzZtYuHChaSnp7siokcoyzjv37+fdevWMWzYMFauXMmePXsYM2YMNpuNp556yhWxK52yjPPQoUM5efIk3bt3xxiDzWbjoYceYuLEia6IXGVc6XPQZrNx6tQp6tevXyH7VXHjJIvF4vDcGHNZ27X0l2JlHbdnn32WJUuWkJqair+/f0XF8xilHefs7GzuvfdeFixYQJ06dVwVz2M48/tcVFREvXr1eP3117FarcTExHD06FGee+45FTe/wZlxTk1NZfr06cybN4/OnTuzd+9eEhMTqV+/PklJSa6IW2WU9HMpqb08qbgppTp16mC1Wi/7X8CJEycuq0ovCgsLc6q/lG2cL5ozZw4zZsxg7dq1tG/fviJjVnrOjvO+ffs4ePAgCQkJ9raioiIAvL292bVrF9HR0RUbuhIqy+9z/fr18fHxwWq12ttatWrFsWPHyM/Px9fXt0IzV0ZlGeekpCSGDx/On/70JwDatWtHbm4uf/7zn5k0aRJeXpq1UR6u9Dno7e1N7dq1K2y/+umVkq+vLzExMaxZs8ahfc2aNXTt2rXEdbp06XJZ/9WrV1+xv5RtnAGee+45pk2bxqeffkpsbGxFx6z0nB3nli1b8s0335Cenm5/3HnnncTHx5Oenk5kZKSrolcqZfl97tatG3v37rUXjwC7d++mfv36KmyuoCzjfO7cucsKGKvViik+0abCslY1V/ocjI2NxcfHp+J2XKHTlT3MxVMNFy5caDIyMszYsWNNYGCgOXjwoDHGmOHDhzvMzN+0aZOxWq1m1qxZZufOnWbWrFk6FbwUnB3n2bNnG19fX7N06VKTmZlpf2RnZ7vrLVQKzo7zpXS2VOk4O86HDx82QUFB5pFHHjG7du0yH330kalXr5555pln3PUWKgVnx/npp582wcHBZsmSJWb//v1m9erVJjo62gwePNhdb6FSyM7ONjt27DA7duwwgHnhhRfMjh077KfcT5w40QwfPtze/+Kp4OPGjTMZGRlm4cKFOhX8evTKK6+YRo0aGV9fX9OpUyezfv16+2txcXHmvvvuc+j/73//27Ro0cL4+PiYli1bmmXLlrk4ceXkzDg3atTIAJc9nn76adcHr2Sc/X3+NRU3pefsOG/evNl07tzZ+Pn5mSZNmpjp06cbm83m4tSVjzPjXFBQYCZPnmyio6ONv7+/iYyMNA8//LA5ffq0G5JXHikpKSX+e3txbO+77z4TFxfnsE5qaqrp2LGj8fX1NVFRUWb+/PkVntNijI6/iYiIiOfQnBsRERHxKCpuRERExKOouBERERGPouJGREREPIqKGxEREfEoKm5ERETEo6i4EREREY+i4kZEREQ8ioobEZFrsHjxYmrUqFHh+4mKisJisWCxWDhz5sxV81zsN3bs2ArPJXI9UnEjUom9+uqrBAcHY7PZ7G05OTn4+PjQo0cPh75paWlYLBZ2795dqm276kO7rCZPnkyHDh1cus+oqCjmzp3r0DZkyJBSj+m1mjp1KpmZmYSEhFyxz5AhQ8jMzKRLly4uySRyPVJxI1KJxcfHk5OTwxdffGFvS0tLIywsjG3btnHu3Dl7e2pqKg0aNKB58+YuzVhYWOhwh+vrjTHGoTh0VrVq1ahXr145Jrqy4OBgwsLCsFgsV80TFhamO4hLlabiRqQSa9GiBQ0aNCA1NdXelpqaSv/+/YmOjmbz5s0O7fHx8fbnL7zwAu3atSMwMJDIyEgefvhhcnJy7H1HjRrF2bNn7V9xTJ48GYD8/HwmTJhAeHg4gYGBdO7c2WH/F4/4fPTRR7Ru3Ro/Pz8OHTp0WfbTp08zbNgw6tatS7Vq1WjWrBmLFi2yv/7444/TvHlzAgICaNKkCUlJSRQUFNj3MWXKFL766it7vsWLF3Pw4EEsFgvp6en27Zw5cwaLxWLPmJqaisViYdWqVcTGxuLn50daWhr79u2jf//+hIaGEhQUxI033sjatWvt2+nVqxeHDh1i3Lhx9n3++v3+2vz584mOjsbX15cWLVrwj3/8w+F1i8XCG2+8wcCBAwkICKBZs2Z88MEHV/w5i4hzVNyIVHK9evUiJSXF/jwlJYVevXoRFxdnb8/Pz2fLli0OxY2Xlxd///vf+fbbb3nrrbdYt24dEyZMAKBr167MnTuX6tWrk5mZSWZmJn/9618BGDVqFJs2bSI5OZmvv/6ae+65h759+7Jnzx77ts+dO8fMmTN54403+O6770o8spGUlERGRgaffPIJO3fuZP78+dSpU8f+enBwMIsXLyYjI4OXXnqJBQsW8OKLLwLFX7089thjtGnTxp5vyJAhTo3bhAkTmDlzJjt37qR9+/bk5OTQr18/1q5dy44dO+jTpw8JCQkcPnwYgOXLlxMREWH/aigzM7PE7a5YsYLExEQee+wxvv32Wx588EFGjRrl8DMCmDJlCoMHD+brr7+mX79+DBs2jJ9//tmp9yAiV1Dh9x0XkQr1+uuvm8DAQFNQUGCysrKMt7e3OX78uElOTjZdu3Y1xhizfv16A5h9+/ZdcTvvvvuuqV27tv35okWLTEhIiEOfvXv3GovFYn788UeH9t69e5snnnjCvh5g0tPTr5o7ISHBjBo1qtTv89lnnzUxMTH2508//bS54YYbHPocOHDAAGbHjh32ttOnTxvApKSkGGOMSUlJMYB57733fnOfrVu3Ni+//LL9eaNGjcyLL77o0OfSceratat54IEHHPrcc889pl+/fvbngHnyySftz3NycozFYjGffPLJFbOUtO+riYuLM4mJiaXuL+JJdORGpJKLj48nNzeXbdu2kZaWRvPmzalXrx5xcXFs27aN3NxcUlNTadiwIU2aNLGvl5KSwm233UZ4eDjBwcGMGDGCn376idzc3Cvu68svv8QYQ/PmzQkKCrI/1q9fz759++z9fH19ad++/VVzP/TQQyQnJ9OhQwcmTJjg8BUawNKlS+nevTthYWEEBQWRlJRkP4pSHmJjYx2e5+bmMmHCBFq3bk2NGjUICgri+++/d3qfO3fupFu3bg5t3bp1Y+fOnQ5tvx6fwMBAgoODOXHihFP7+vXPYPTo0U6tK+LJvN0dQESuTdOmTYmIiCAlJYXTp08TFxcHQFhYGI0bN2bTpk2kpKRwyy232Nc5dOgQ/fr1Y/To0UybNo1atWqxceNG7r//fvu8lpIUFRVhtVrZvn07VqvV4bWgoCD7crVq1a466RXgjjvu4NChQ3z88cesXbuW3r17M2bMGObMmcPWrVsZOnQoU6ZMoU+fPoSEhJCcnMzzzz9/1W16eRX/f80YY2+70vsJDAx0eD5+/HhWrVrFnDlzaNq0KdWqVePuu+8mPz//qvssyaXv3RhzWZuPj89l6zg78frXc4uqV6/uZEoRz6XiRsQDxMfHk5qayunTpxk/fry9PS4ujlWrVrF161ZGjRplb//iiy+w2Ww8//zz9oLg3Xffddimr68vhYWFDm0dO3aksLCQEydOXHaqeVnUrVuXkSNHMnLkSHr06MH48eOZM2cOmzZtolGjRkyaNMne99JJySXlq1u3LgCZmZl07NgRcCwAriYtLY2RI0cycOBAoPiU+oMHD/7mPi/VqlUrNm7cyIgRI+xtmzdvplWrVqXK4YymTZuW+zZFPIGKGxEPEB8fz5gxYygoKLAfuYHi4uahhx7iwoULDpOJo6OjsdlsvPzyyyQkJLBp0yZeffVVh21GRUWRk5PDf/7zH2644QYCAgJo3rw5w4YNY8SIETz//PN07NiRU6dOsW7dOtq1a0e/fv1Knfmpp54iJiaGNm3akJeXx0cffWQvAJo2bcrhw4dJTk7mxhtv5OOPP2bFihWX5Ttw4ADp6elEREQQHBxMtWrVuPnmm5k1axZRUVGcOnWKJ598slR5mjZtyvLly0lISMBisZCUlHTZkZSoqCg2bNjA0KFD8fPzc5gAfdH48eMZPHgwnTp1onfv3nz44YcsX77c4cwrEalYmnMj4gHi4+M5f/48TZs2JTQ01N4eFxdHdnY20dHRREZG2ts7dOjACy+8wOzZs2nbti3vvPMOM2fOdNhm165dGT16NEOGDKFu3bo8++yzACxatIgRI0bw2GOP0aJFC+68804+++wzh+2Xhq+vL0888QTt27enZ8+eWK1WkpOTAejfvz/jxo3jkUceoUOHDmzevJmkpCSH9e+66y769u1LfHw8devWZcmSJQC8+eabFBQUEBsbS2JiIs8880yp8rz44ovUrFmTrl27kpCQQJ8+fejUqZNDn6lTp3Lw4EGio6PtR4kuNWDAAF566SWee+452rRpw2uvvcaiRYvo1auXU+MjImVnMb/+clpERK5LUVFRjB07ttS3VOjVqxcdOnS47IrKIlWBjtyIiFQSjz/+OEFBQZw9e/aKfd555x2CgoJIS0tzYTKR64uO3IiIVAKHDh2yn/nVpEkT+0TwS2VnZ3O2lpaKAAAAVUlEQVT8+HEAatSoUeK8IBFPp+JGREREPIq+lhIRERGPouJGREREPIqKGxEREfEoKm5ERETEo6i4EREREY+i4kZEREQ8ioobERER8SgqbkRERMSj/B/fbLLbSmAPJAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x0000000032B77B38>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the problem\n",
    "# relative permeabilities\n",
    "rel_perms = FF.oil_water_rel_perms(krw0=0.4, kro0=0.9, \n",
    "    swc=0.15, sor=0.2, nw=2.0, no = 2.0)\n",
    "# FF.visualize(rel_perms)\n",
    "# define the fluids\n",
    "fluids = FF.oil_water_fluids(mu_water=1e-3, mu_oil=1e-3)\n",
    "\n",
    "# define the fractional flow functions\n",
    "fw, dfw = FF.fractional_flow_function(rel_perms, fluids)\n",
    "# visualize the fractional flow\n",
    "# FF.visualize(rel_perms, fluids, label=\"lowsal\")\n",
    "# tight_layout()\n",
    "ut = 1.15e-5\n",
    "phi = 0.3\n",
    "L = 0.15\n",
    "core_flood = FF.core_flooding(u_inj=1.15e-5, pv_inject=5, \n",
    "    p_back=1e5, sw_init=0.2, sw_inj=1.0, rel_perms=rel_perms)\n",
    "core_props = FF.core_properties()\n",
    "wf_res = FF.water_flood(core_props, fluids, rel_perms, core_flood)\n",
    "fw, dfw = FF.fractional_flow_function(rel_perms, fluids)\n",
    "sw_tmp = FF.linspace(0,1,100)\n",
    "\n",
    "FF.visualize(wf_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:09\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "t_sec, pv, rec_fact, dp_core, x, sw_face, c_face, c_out_sal=FF.water_flood_numeric(core_props, fluids, rel_perms, core_flood, Nx=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# synthetic experimental data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 800x500 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(Figure(PyObject <Figure size 800x500 with 2 Axes>), PyObject <matplotlib.axes._subplots.AxesSubplot object at 0x0000000032D38898>, PyObject <matplotlib.axes._subplots.AxesSubplot object at 0x0000000032DFF278>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_exp_dp = wf_res.dp_time[:,1]\n",
    "dp_exp = wf_res.dp_time[:,2]\n",
    "t_exp_R = wf_res.recovery_time[:,1]\n",
    "R_exp = wf_res.recovery_time[:,2]\n",
    "plotyy(t_exp_R, R_exp, t_exp_dp, dp_exp, fig_size = [8,5], x_label=\"time [s]\", y1_label=\"R [-]\", y2_label=\"dP [Pa]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x0000000031D666D8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(t_exp_dp, dp_exp, \"o\", t_sec, dp_core)\n",
    "legend([\"Analytical\", \"Numerical\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# define the objective function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# struct\n",
    "struct exp_data\n",
    "    t_exp_dp\n",
    "    dp_exp\n",
    "    t_exp_R\n",
    "    R_exp\n",
    "end\n",
    "exp_data1 = exp_data(t_exp_dp, dp_exp, t_exp_R, R_exp);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4.57679065373876"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "rel_perm_param [krw0, kro0, nw, no, swc, sor]\n",
    "\"\"\"\n",
    "function error_calc(rel_perm_param, exp_data, core_props, fluids, core_flood; w_p=1.0, w_R=1.0)\n",
    "    rel_perms = FF.oil_water_rel_perms(krw0=rel_perm_param[1], kro0=rel_perm_param[2], \n",
    "    swc=rel_perm_param[5], sor=rel_perm_param[6], nw=rel_perm_param[3], no = rel_perm_param[4])\n",
    "    wf_res = FF.water_flood(core_props, fluids, rel_perms, core_flood)\n",
    "    dp_calc = Spline1D(wf_res.dp_time[:,1], wf_res.dp_time[:,2], k=1, bc=\"nearest\")\n",
    "    R_calc = Spline1D(wf_res.recovery_time[:,1], wf_res.recovery_time[:,2], k=1, bc=\"nearest\")\n",
    "    error_dp = abs.(dp_calc(exp_data.t_exp_dp) .- exp_data.dp_exp)\n",
    "#     println(error_dp)\n",
    "    error_R = abs.(R_calc(exp_data.t_exp_R) .- exp_data.R_exp)\n",
    "#     println(error_R)\n",
    "    error_dp_norm = w_p.*error_dp./exp_data.dp_exp\n",
    "    error_R_norm = w_R.*error_R./(exp_data.R_exp.+eps())\n",
    "    return mean(error_R_norm)+mean(error_dp_norm)\n",
    "end\n",
    "\n",
    "function vis_error(rel_perm_param, exp_data, core_props, fluids, core_flood)\n",
    "    rel_perms = FF.oil_water_rel_perms(krw0=rel_perm_param[1], kro0=rel_perm_param[2], \n",
    "    swc=rel_perm_param[5], sor=rel_perm_param[6], nw=rel_perm_param[3], no = rel_perm_param[4])\n",
    "    wf_res = FF.water_flood(core_props, fluids, rel_perms, core_flood)\n",
    "    figure()\n",
    "    plot(wf_res.dp_time[:,1], wf_res.dp_time[:,2],  exp_data.t_exp_dp, exp_data.dp_exp, \"o\")\n",
    "    xlabel(\"t [s]\")\n",
    "    ylabel(\"dp [Pa]\")\n",
    "    legend([\"Theoretical\", \"Experiment\"])\n",
    "    \n",
    "    figure()\n",
    "    plot(wf_res.recovery_time[:,1], wf_res.recovery_time[:,2], exp_data.t_exp_R, exp_data.R_exp, \"v\")\n",
    "    xlabel(\"t [s]\")\n",
    "    ylabel(\"R [-]\")\n",
    "    legend([\"Theoretical\", \"Experiment\"])\n",
    "    \n",
    "end\n",
    "\n",
    "# test\n",
    "x_init = [0.109681, 0.201297, 3.96653, 3.0, 0.19, 0.262231]\n",
    "\n",
    "vis_error(x_init, exp_data1, core_props, fluids, core_flood)\n",
    "error_calc(x_init, exp_data1, core_props, fluids, core_flood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# define the objective function and gradients and weight factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0713534659832842"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weight factors:\n",
    "w_p = ones(length(exp_data1.dp_exp))\n",
    "temp_val, ind_max = findmax(exp_data1.dp_exp)\n",
    "println(ind_max)\n",
    "w_p[ind_max-3:ind_max+3] .= 10\n",
    "w_p[end-10:end] .= 10\n",
    "w_p[1]=10\n",
    "w_R = ones(length(exp_data1.R_exp))\n",
    "w_R[20:25] .= 10\n",
    "w_R[end-3:end] .= 10\n",
    "\n",
    "\n",
    "function f(x)\n",
    "    f_val = 0.0\n",
    "    try\n",
    "        f_val = error_calc(x, exp_data1, core_props, fluids, core_flood, w_p = w_p, w_R = w_R)\n",
    "    catch\n",
    "        f_val = 100.0\n",
    "#         info(\"Objective function did not converge!\")\n",
    "    end\n",
    "    return f_val\n",
    "end\n",
    "\n",
    "    \n",
    "function g(x)\n",
    "    eps1 = 1e-3\n",
    "    f_val = f(x)\n",
    "    g_val = ones(length(x))\n",
    "    try\n",
    "        # g_val = Calculus.gradient(x -> error_calc(x, exp_data1, core_props, fluids, core_flood), x)\n",
    "        for j in eachindex(x)\n",
    "            x2 = copy(x)\n",
    "            x2[j]+=eps1\n",
    "            f_val2 = f(x2)\n",
    "            g_val[j] = (f_val2-f_val)/eps1\n",
    "        end\n",
    "    catch\n",
    "        g_val = ones(length(x))\n",
    "    end\n",
    "    return g_val\n",
    "end\n",
    "\n",
    "function obj_fun(param, grad)\n",
    "    if length(grad)>0\n",
    "      grad[:] = g(param)\n",
    "    end\n",
    "    \n",
    "    obj_fun_val = f(param)\n",
    "    if isnan(obj_fun_val) || isinf(obj_fun_val)\n",
    "        obj_fun_val = 100.0\n",
    "    end\n",
    "    return obj_fun_val\n",
    "end\n",
    "\n",
    "# test\n",
    "grad_x = zeros(6)\n",
    "obj_fun([1.0, 0.8, 3, 4, 0.2, 0.2], grad_x)\n",
    "\n",
    "f([1.0, 0.8, 2, 2, 0.1, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6-element Array{Float64,1}:\n",
       "  0.559755747851276   \n",
       "  0.007055679254075464\n",
       " -0.23785704697598664 \n",
       " -0.04586750588075894 \n",
       " -0.8425478566016498  \n",
       "  1.8982879302710254  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting optimization with optimizer DiffEvoOpt{FitPopulation{Float64},RadiusLimitedSelector,BlackBoxOptim.AdaptiveDiffEvoRandBin{3},RandomBound{ContinuousRectSearchSpace}}\n",
      "0.00 secs, 0 evals, 0 steps\n",
      "0.58 secs, 10 evals, 5 steps, improv/step: 0.800 (last = 0.8000), fitness=0.611312982\n",
      "1.14 secs, 21 evals, 11 steps, improv/step: 0.727 (last = 0.6667), fitness=0.529325847\n",
      "1.66 secs, 33 evals, 18 steps, improv/step: 0.556 (last = 0.2857), fitness=0.415254671\n",
      "2.19 secs, 44 evals, 24 steps, improv/step: 0.500 (last = 0.3333), fitness=0.282775545\n",
      "2.71 secs, 56 evals, 31 steps, improv/step: 0.516 (last = 0.5714), fitness=0.282775545\n",
      "3.25 secs, 68 evals, 37 steps, improv/step: 0.514 (last = 0.5000), fitness=0.179274381\n",
      "3.77 secs, 80 evals, 44 steps, improv/step: 0.523 (last = 0.5714), fitness=0.179274381\n",
      "4.32 secs, 92 evals, 52 steps, improv/step: 0.500 (last = 0.3750), fitness=0.179274381\n",
      "4.84 secs, 104 evals, 59 steps, improv/step: 0.508 (last = 0.5714), fitness=0.179274381\n",
      "5.38 secs, 116 evals, 68 steps, improv/step: 0.529 (last = 0.6667), fitness=0.179274381\n",
      "5.90 secs, 127 evals, 76 steps, improv/step: 0.539 (last = 0.6250), fitness=0.096299767\n",
      "6.55 secs, 139 evals, 84 steps, improv/step: 0.512 (last = 0.2500), fitness=0.096299767\n",
      "7.10 secs, 151 evals, 92 steps, improv/step: 0.522 (last = 0.6250), fitness=0.096299767\n",
      "7.67 secs, 164 evals, 101 steps, improv/step: 0.485 (last = 0.1111), fitness=0.096299767\n",
      "8.24 secs, 177 evals, 110 steps, improv/step: 0.482 (last = 0.4444), fitness=0.096299767\n",
      "8.77 secs, 189 evals, 120 steps, improv/step: 0.450 (last = 0.1000), fitness=0.096299767\n",
      "9.30 secs, 200 evals, 130 steps, improv/step: 0.446 (last = 0.4000), fitness=0.096299767\n",
      "9.85 secs, 212 evals, 139 steps, improv/step: 0.432 (last = 0.2222), fitness=0.096299767\n",
      "10.42 secs, 224 evals, 149 steps, improv/step: 0.436 (last = 0.5000), fitness=0.096299767\n",
      "10.94 secs, 236 evals, 159 steps, improv/step: 0.440 (last = 0.5000), fitness=0.096299767\n",
      "11.50 secs, 248 evals, 169 steps, improv/step: 0.444 (last = 0.5000), fitness=0.096299767\n",
      "12.04 secs, 260 evals, 181 steps, improv/step: 0.448 (last = 0.5000), fitness=0.096299767\n",
      "12.61 secs, 271 evals, 191 steps, improv/step: 0.435 (last = 0.2000), fitness=0.096299767\n",
      "13.14 secs, 281 evals, 200 steps, improv/step: 0.435 (last = 0.4444), fitness=0.096299767\n",
      "13.65 secs, 292 evals, 210 steps, improv/step: 0.429 (last = 0.3000), fitness=0.096299767\n",
      "14.19 secs, 303 evals, 220 steps, improv/step: 0.432 (last = 0.5000), fitness=0.096299767\n",
      "14.71 secs, 314 evals, 231 steps, improv/step: 0.433 (last = 0.4545), fitness=0.096299767\n",
      "15.26 secs, 324 evals, 239 steps, improv/step: 0.435 (last = 0.5000), fitness=0.096299767\n",
      "16.00 secs, 328 evals, 241 steps, improv/step: 0.436 (last = 0.5000), fitness=0.096299767\n",
      "16.53 secs, 336 evals, 249 steps, improv/step: 0.442 (last = 0.6250), fitness=0.096299767\n",
      "17.04 secs, 347 evals, 260 steps, improv/step: 0.438 (last = 0.3636), fitness=0.096299767\n",
      "17.55 secs, 359 evals, 271 steps, improv/step: 0.432 (last = 0.2727), fitness=0.096299767\n",
      "18.09 secs, 371 evals, 283 steps, improv/step: 0.428 (last = 0.3333), fitness=0.096299767\n",
      "18.60 secs, 383 evals, 295 steps, improv/step: 0.420 (last = 0.2500), fitness=0.096299767\n",
      "19.14 secs, 395 evals, 307 steps, improv/step: 0.410 (last = 0.1667), fitness=0.096299767\n",
      "19.66 secs, 407 evals, 318 steps, improv/step: 0.403 (last = 0.1818), fitness=0.096299767\n",
      "20.16 secs, 418 evals, 329 steps, improv/step: 0.398 (last = 0.2727), fitness=0.096299767\n",
      "20.68 secs, 430 evals, 341 steps, improv/step: 0.399 (last = 0.4167), fitness=0.096299767\n",
      "21.18 secs, 441 evals, 352 steps, improv/step: 0.398 (last = 0.3636), fitness=0.096299767\n",
      "21.71 secs, 453 evals, 364 steps, improv/step: 0.393 (last = 0.2500), fitness=0.096299767\n",
      "22.22 secs, 464 evals, 375 steps, improv/step: 0.387 (last = 0.1818), fitness=0.096299767\n",
      "22.73 secs, 471 evals, 382 steps, improv/step: 0.382 (last = 0.1429), fitness=0.096299767\n",
      "23.23 secs, 478 evals, 389 steps, improv/step: 0.380 (last = 0.2857), fitness=0.096299767\n",
      "23.74 secs, 489 evals, 400 steps, improv/step: 0.375 (last = 0.1818), fitness=0.096299767\n",
      "24.27 secs, 501 evals, 412 steps, improv/step: 0.369 (last = 0.1667), fitness=0.096299767\n",
      "24.77 secs, 512 evals, 423 steps, improv/step: 0.369 (last = 0.3636), fitness=0.096299767\n",
      "25.30 secs, 524 evals, 435 steps, improv/step: 0.361 (last = 0.0833), fitness=0.096299767\n",
      "25.85 secs, 535 evals, 446 steps, improv/step: 0.363 (last = 0.4545), fitness=0.096299767\n",
      "26.38 secs, 546 evals, 457 steps, improv/step: 0.359 (last = 0.1818), fitness=0.096299767\n",
      "26.90 secs, 558 evals, 469 steps, improv/step: 0.356 (last = 0.2500), fitness=0.091782119\n",
      "27.44 secs, 570 evals, 481 steps, improv/step: 0.351 (last = 0.1667), fitness=0.091782119\n",
      "27.96 secs, 581 evals, 492 steps, improv/step: 0.346 (last = 0.0909), fitness=0.091782119\n",
      "28.46 secs, 592 evals, 503 steps, improv/step: 0.342 (last = 0.1818), fitness=0.091782119\n",
      "28.99 secs, 604 evals, 515 steps, improv/step: 0.340 (last = 0.2500), fitness=0.091782119\n",
      "29.53 secs, 616 evals, 527 steps, improv/step: 0.342 (last = 0.4167), fitness=0.082123283\n",
      "30.07 secs, 628 evals, 539 steps, improv/step: 0.338 (last = 0.1667), fitness=0.082123283\n",
      "30.60 secs, 640 evals, 551 steps, improv/step: 0.332 (last = 0.0833), fitness=0.082123283\n",
      "31.13 secs, 652 evals, 563 steps, improv/step: 0.330 (last = 0.2500), fitness=0.082123283\n",
      "31.66 secs, 664 evals, 575 steps, improv/step: 0.329 (last = 0.2500), fitness=0.082123283\n",
      "32.18 secs, 676 evals, 587 steps, improv/step: 0.324 (last = 0.0833), fitness=0.082123283\n",
      "32.68 secs, 687 evals, 598 steps, improv/step: 0.326 (last = 0.4545), fitness=0.082123283\n",
      "33.22 secs, 699 evals, 610 steps, improv/step: 0.321 (last = 0.0833), fitness=0.082123283\n",
      "33.76 secs, 711 evals, 622 steps, improv/step: 0.318 (last = 0.1667), fitness=0.082123283\n",
      "34.27 secs, 723 evals, 634 steps, improv/step: 0.315 (last = 0.1667), fitness=0.082123283\n",
      "34.82 secs, 730 evals, 641 steps, improv/step: 0.317 (last = 0.4286), fitness=0.081739619\n",
      "35.34 secs, 742 evals, 653 steps, improv/step: 0.314 (last = 0.1667), fitness=0.081739619\n",
      "35.87 secs, 753 evals, 664 steps, improv/step: 0.312 (last = 0.1818), fitness=0.081739619\n",
      "36.41 secs, 762 evals, 673 steps, improv/step: 0.312 (last = 0.3333), fitness=0.081739619\n",
      "36.96 secs, 772 evals, 683 steps, improv/step: 0.313 (last = 0.4000), fitness=0.081711681\n",
      "37.50 secs, 782 evals, 693 steps, improv/step: 0.310 (last = 0.1000), fitness=0.081711681\n",
      "38.04 secs, 794 evals, 705 steps, improv/step: 0.309 (last = 0.2500), fitness=0.081711681\n",
      "38.55 secs, 805 evals, 716 steps, improv/step: 0.307 (last = 0.1818), fitness=0.081711681\n",
      "39.08 secs, 817 evals, 728 steps, improv/step: 0.305 (last = 0.1667), fitness=0.081711681\n",
      "39.62 secs, 829 evals, 740 steps, improv/step: 0.305 (last = 0.3333), fitness=0.042690271\n",
      "40.14 secs, 841 evals, 752 steps, improv/step: 0.305 (last = 0.2500), fitness=0.042690271\n",
      "40.68 secs, 853 evals, 764 steps, improv/step: 0.300 (last = 0.0000), fitness=0.042690271\n",
      "41.21 secs, 865 evals, 776 steps, improv/step: 0.300 (last = 0.3333), fitness=0.042690271\n",
      "41.73 secs, 877 evals, 788 steps, improv/step: 0.301 (last = 0.3333), fitness=0.042690271\n",
      "42.25 secs, 889 evals, 800 steps, improv/step: 0.299 (last = 0.1667), fitness=0.042690271\n",
      "42.80 secs, 899 evals, 810 steps, improv/step: 0.300 (last = 0.4000), fitness=0.042690271\n",
      "43.33 secs, 911 evals, 822 steps, improv/step: 0.299 (last = 0.2500), fitness=0.042690271\n",
      "43.85 secs, 923 evals, 834 steps, improv/step: 0.299 (last = 0.2500), fitness=0.042690271\n",
      "44.36 secs, 931 evals, 842 steps, improv/step: 0.299 (last = 0.3750), fitness=0.042690271\n",
      "44.88 secs, 943 evals, 854 steps, improv/step: 0.301 (last = 0.4167), fitness=0.042690271\n",
      "45.40 secs, 955 evals, 866 steps, improv/step: 0.298 (last = 0.0833), fitness=0.042690271\n",
      "45.90 secs, 966 evals, 877 steps, improv/step: 0.296 (last = 0.1818), fitness=0.042690271\n",
      "46.42 secs, 977 evals, 888 steps, improv/step: 0.295 (last = 0.1818), fitness=0.042690271\n",
      "46.95 secs, 989 evals, 900 steps, improv/step: 0.294 (last = 0.2500), fitness=0.041974691\n",
      "47.48 secs, 1001 evals, 912 steps, improv/step: 0.294 (last = 0.2500), fitness=0.041974691\n",
      "48.01 secs, 1013 evals, 924 steps, improv/step: 0.291 (last = 0.0833), fitness=0.041974691\n",
      "48.55 secs, 1025 evals, 936 steps, improv/step: 0.290 (last = 0.1667), fitness=0.041974691\n",
      "49.09 secs, 1037 evals, 948 steps, improv/step: 0.288 (last = 0.1667), fitness=0.041974691\n",
      "49.61 secs, 1049 evals, 960 steps, improv/step: 0.284 (last = 0.0000), fitness=0.041974691\n",
      "50.15 secs, 1061 evals, 972 steps, improv/step: 0.286 (last = 0.4167), fitness=0.041974691\n",
      "50.66 secs, 1073 evals, 984 steps, improv/step: 0.286 (last = 0.2500), fitness=0.037742314\n",
      "51.18 secs, 1085 evals, 996 steps, improv/step: 0.285 (last = 0.2500), fitness=0.037742314\n",
      "51.70 secs, 1097 evals, 1008 steps, improv/step: 0.285 (last = 0.2500), fitness=0.037742314\n",
      "52.22 secs, 1109 evals, 1020 steps, improv/step: 0.282 (last = 0.0833), fitness=0.037742314\n",
      "52.73 secs, 1121 evals, 1032 steps, improv/step: 0.281 (last = 0.1667), fitness=0.037742314\n",
      "53.27 secs, 1133 evals, 1044 steps, improv/step: 0.278 (last = 0.0000), fitness=0.037742314\n",
      "53.80 secs, 1145 evals, 1056 steps, improv/step: 0.277 (last = 0.1667), fitness=0.037742314\n",
      "54.33 secs, 1157 evals, 1068 steps, improv/step: 0.276 (last = 0.2500), fitness=0.037742314\n",
      "54.84 secs, 1169 evals, 1080 steps, improv/step: 0.276 (last = 0.2500), fitness=0.037742314\n",
      "55.38 secs, 1180 evals, 1091 steps, improv/step: 0.275 (last = 0.1818), fitness=0.037742314\n",
      "55.90 secs, 1192 evals, 1103 steps, improv/step: 0.276 (last = 0.3333), fitness=0.037742314\n",
      "56.44 secs, 1203 evals, 1114 steps, improv/step: 0.275 (last = 0.1818), fitness=0.037742314\n",
      "56.96 secs, 1215 evals, 1126 steps, improv/step: 0.275 (last = 0.3333), fitness=0.037742314\n",
      "57.50 secs, 1227 evals, 1138 steps, improv/step: 0.276 (last = 0.3333), fitness=0.037742314\n",
      "58.02 secs, 1239 evals, 1150 steps, improv/step: 0.275 (last = 0.1667), fitness=0.037742314\n",
      "58.55 secs, 1249 evals, 1160 steps, improv/step: 0.273 (last = 0.1000), fitness=0.037742314\n",
      "59.07 secs, 1261 evals, 1172 steps, improv/step: 0.271 (last = 0.0833), fitness=0.037742314\n",
      "59.60 secs, 1273 evals, 1184 steps, improv/step: 0.270 (last = 0.1667), fitness=0.037742314\n",
      "60.13 secs, 1285 evals, 1196 steps, improv/step: 0.268 (last = 0.0833), fitness=0.037742314\n",
      "60.65 secs, 1297 evals, 1208 steps, improv/step: 0.267 (last = 0.1667), fitness=0.037742314\n",
      "61.17 secs, 1309 evals, 1220 steps, improv/step: 0.267 (last = 0.2500), fitness=0.037742314\n",
      "61.73 secs, 1321 evals, 1232 steps, improv/step: 0.267 (last = 0.2500), fitness=0.037399579\n",
      "62.25 secs, 1333 evals, 1244 steps, improv/step: 0.267 (last = 0.2500), fitness=0.037399579\n",
      "62.76 secs, 1345 evals, 1256 steps, improv/step: 0.267 (last = 0.2500), fitness=0.037399579\n",
      "63.27 secs, 1356 evals, 1267 steps, improv/step: 0.266 (last = 0.1818), fitness=0.035683268\n",
      "63.82 secs, 1365 evals, 1276 steps, improv/step: 0.266 (last = 0.2222), fitness=0.035683268\n",
      "64.35 secs, 1373 evals, 1284 steps, improv/step: 0.266 (last = 0.3750), fitness=0.035683268\n",
      "64.86 secs, 1380 evals, 1291 steps, improv/step: 0.265 (last = 0.0000), fitness=0.035683268\n",
      "65.36 secs, 1387 evals, 1298 steps, improv/step: 0.265 (last = 0.2857), fitness=0.035683268\n",
      "65.89 secs, 1393 evals, 1304 steps, improv/step: 0.265 (last = 0.1667), fitness=0.035683268\n",
      "66.41 secs, 1400 evals, 1311 steps, improv/step: 0.265 (last = 0.2857), fitness=0.035369366\n",
      "66.91 secs, 1406 evals, 1317 steps, improv/step: 0.266 (last = 0.5000), fitness=0.035369366\n",
      "67.49 secs, 1414 evals, 1325 steps, improv/step: 0.265 (last = 0.1250), fitness=0.035369366\n",
      "68.01 secs, 1421 evals, 1332 steps, improv/step: 0.264 (last = 0.1429), fitness=0.035369366\n",
      "68.52 secs, 1428 evals, 1339 steps, improv/step: 0.264 (last = 0.1429), fitness=0.035369366\n",
      "69.08 secs, 1435 evals, 1346 steps, improv/step: 0.263 (last = 0.1429), fitness=0.035369366\n",
      "69.61 secs, 1441 evals, 1352 steps, improv/step: 0.264 (last = 0.5000), fitness=0.035369366\n",
      "70.17 secs, 1449 evals, 1360 steps, improv/step: 0.265 (last = 0.3750), fitness=0.035369366\n",
      "70.70 secs, 1458 evals, 1369 steps, improv/step: 0.267 (last = 0.5556), fitness=0.035369366\n",
      "71.22 secs, 1468 evals, 1379 steps, improv/step: 0.265 (last = 0.1000), fitness=0.035369366\n",
      "71.75 secs, 1480 evals, 1391 steps, improv/step: 0.263 (last = 0.0000), fitness=0.035369366\n",
      "72.29 secs, 1492 evals, 1403 steps, improv/step: 0.263 (last = 0.2500), fitness=0.024901854\n",
      "72.84 secs, 1503 evals, 1414 steps, improv/step: 0.263 (last = 0.2727), fitness=0.024901854\n",
      "73.34 secs, 1509 evals, 1420 steps, improv/step: 0.263 (last = 0.1667), fitness=0.024901854\n",
      "73.91 secs, 1513 evals, 1424 steps, improv/step: 0.262 (last = 0.0000), fitness=0.024901854\n",
      "74.42 secs, 1523 evals, 1434 steps, improv/step: 0.262 (last = 0.3000), fitness=0.024901854\n",
      "75.04 secs, 1534 evals, 1445 steps, improv/step: 0.262 (last = 0.1818), fitness=0.024901854\n",
      "75.58 secs, 1545 evals, 1456 steps, improv/step: 0.261 (last = 0.1818), fitness=0.024901854\n",
      "76.11 secs, 1557 evals, 1468 steps, improv/step: 0.262 (last = 0.4167), fitness=0.024901854\n",
      "76.64 secs, 1567 evals, 1478 steps, improv/step: 0.263 (last = 0.4000), fitness=0.024901854\n",
      "77.17 secs, 1579 evals, 1490 steps, improv/step: 0.262 (last = 0.0833), fitness=0.024901854\n",
      "77.72 secs, 1591 evals, 1502 steps, improv/step: 0.261 (last = 0.1667), fitness=0.024901854\n",
      "78.23 secs, 1603 evals, 1514 steps, improv/step: 0.264 (last = 0.6667), fitness=0.024901854\n",
      "78.76 secs, 1615 evals, 1526 steps, improv/step: 0.263 (last = 0.1667), fitness=0.024901854\n",
      "79.28 secs, 1627 evals, 1538 steps, improv/step: 0.263 (last = 0.2500), fitness=0.024901854\n",
      "79.79 secs, 1636 evals, 1547 steps, improv/step: 0.262 (last = 0.1111), fitness=0.024901854\n",
      "80.32 secs, 1648 evals, 1559 steps, improv/step: 0.262 (last = 0.1667), fitness=0.024901854\n",
      "80.87 secs, 1658 evals, 1569 steps, improv/step: 0.262 (last = 0.3000), fitness=0.024901854\n",
      "81.39 secs, 1670 evals, 1581 steps, improv/step: 0.261 (last = 0.1667), fitness=0.024901854\n",
      "81.92 secs, 1682 evals, 1593 steps, improv/step: 0.261 (last = 0.1667), fitness=0.024901854\n",
      "82.44 secs, 1694 evals, 1605 steps, improv/step: 0.260 (last = 0.2500), fitness=0.024901854\n",
      "82.97 secs, 1704 evals, 1615 steps, improv/step: 0.261 (last = 0.3000), fitness=0.024901854\n",
      "83.54 secs, 1712 evals, 1623 steps, improv/step: 0.261 (last = 0.2500), fitness=0.024901854\n",
      "84.05 secs, 1721 evals, 1632 steps, improv/step: 0.261 (last = 0.3333), fitness=0.024901854\n",
      "84.56 secs, 1732 evals, 1643 steps, improv/step: 0.259 (last = 0.0000), fitness=0.024901854\n",
      "85.06 secs, 1743 evals, 1654 steps, improv/step: 0.258 (last = 0.0000), fitness=0.024901854\n",
      "85.59 secs, 1754 evals, 1665 steps, improv/step: 0.256 (last = 0.0909), fitness=0.024901854\n",
      "86.12 secs, 1765 evals, 1676 steps, improv/step: 0.257 (last = 0.3636), fitness=0.024901854\n",
      "86.66 secs, 1776 evals, 1687 steps, improv/step: 0.258 (last = 0.3636), fitness=0.018611812\n",
      "87.17 secs, 1780 evals, 1691 steps, improv/step: 0.257 (last = 0.0000), fitness=0.018611812\n",
      "87.71 secs, 1786 evals, 1697 steps, improv/step: 0.256 (last = 0.0000), fitness=0.018611812\n",
      "88.24 secs, 1793 evals, 1704 steps, improv/step: 0.255 (last = 0.0000), fitness=0.018611812\n",
      "88.74 secs, 1801 evals, 1712 steps, improv/step: 0.255 (last = 0.1250), fitness=0.018611812\n",
      "89.33 secs, 1809 evals, 1720 steps, improv/step: 0.254 (last = 0.1250), fitness=0.018611812\n",
      "89.88 secs, 1815 evals, 1726 steps, improv/step: 0.254 (last = 0.1667), fitness=0.018611812\n",
      "90.42 secs, 1822 evals, 1733 steps, improv/step: 0.253 (last = 0.0000), fitness=0.018611812\n",
      "90.97 secs, 1829 evals, 1740 steps, improv/step: 0.253 (last = 0.4286), fitness=0.018611812\n",
      "91.65 secs, 1836 evals, 1747 steps, improv/step: 0.254 (last = 0.2857), fitness=0.018611812\n",
      "92.19 secs, 1840 evals, 1751 steps, improv/step: 0.254 (last = 0.5000), fitness=0.018611812\n",
      "92.72 secs, 1848 evals, 1759 steps, improv/step: 0.253 (last = 0.0000), fitness=0.018611812\n",
      "93.23 secs, 1859 evals, 1770 steps, improv/step: 0.253 (last = 0.2727), fitness=0.018611812\n",
      "93.75 secs, 1870 evals, 1781 steps, improv/step: 0.252 (last = 0.0000), fitness=0.018611812\n",
      "94.29 secs, 1879 evals, 1790 steps, improv/step: 0.250 (last = 0.0000), fitness=0.018611812\n",
      "94.80 secs, 1890 evals, 1801 steps, improv/step: 0.250 (last = 0.1818), fitness=0.018611812\n",
      "95.32 secs, 1899 evals, 1810 steps, improv/step: 0.251 (last = 0.4444), fitness=0.018611812\n",
      "95.83 secs, 1911 evals, 1822 steps, improv/step: 0.250 (last = 0.1667), fitness=0.018611812\n",
      "96.37 secs, 1923 evals, 1834 steps, improv/step: 0.250 (last = 0.2500), fitness=0.018611812\n",
      "96.87 secs, 1932 evals, 1843 steps, improv/step: 0.250 (last = 0.2222), fitness=0.018611812\n",
      "97.40 secs, 1944 evals, 1855 steps, improv/step: 0.251 (last = 0.3333), fitness=0.018611812\n",
      "97.91 secs, 1952 evals, 1863 steps, improv/step: 0.251 (last = 0.2500), fitness=0.018611812\n",
      "98.56 secs, 1961 evals, 1872 steps, improv/step: 0.251 (last = 0.2222), fitness=0.018611812\n",
      "99.07 secs, 1969 evals, 1880 steps, improv/step: 0.251 (last = 0.3750), fitness=0.018611812\n",
      "99.58 secs, 1980 evals, 1891 steps, improv/step: 0.251 (last = 0.1818), fitness=0.018611812\n",
      "100.11 secs, 1992 evals, 1903 steps, improv/step: 0.250 (last = 0.1667), fitness=0.018611812\n",
      "100.62 secs, 2001 evals, 1912 steps, improv/step: 0.250 (last = 0.2222), fitness=0.018611812\n",
      "101.14 secs, 2012 evals, 1923 steps, improv/step: 0.250 (last = 0.2727), fitness=0.018611812\n",
      "101.67 secs, 2023 evals, 1934 steps, improv/step: 0.249 (last = 0.0000), fitness=0.018611812\n",
      "102.19 secs, 2035 evals, 1946 steps, improv/step: 0.248 (last = 0.0833), fitness=0.018611812\n",
      "102.70 secs, 2046 evals, 1957 steps, improv/step: 0.247 (last = 0.1818), fitness=0.018611812\n",
      "103.23 secs, 2056 evals, 1967 steps, improv/step: 0.246 (last = 0.0000), fitness=0.018611812\n",
      "103.86 secs, 2060 evals, 1971 steps, improv/step: 0.247 (last = 0.5000), fitness=0.018611812\n",
      "104.38 secs, 2065 evals, 1976 steps, improv/step: 0.246 (last = 0.2000), fitness=0.018611812\n",
      "104.94 secs, 2075 evals, 1986 steps, improv/step: 0.247 (last = 0.3000), fitness=0.018611812\n",
      "105.46 secs, 2086 evals, 1997 steps, improv/step: 0.245 (last = 0.0000), fitness=0.018611812\n",
      "105.98 secs, 2097 evals, 2008 steps, improv/step: 0.244 (last = 0.0000), fitness=0.018611812\n",
      "106.50 secs, 2108 evals, 2019 steps, improv/step: 0.244 (last = 0.1818), fitness=0.018611812\n",
      "107.00 secs, 2119 evals, 2030 steps, improv/step: 0.242 (last = 0.0000), fitness=0.018611812\n",
      "107.50 secs, 2129 evals, 2040 steps, improv/step: 0.241 (last = 0.0000), fitness=0.018611812\n",
      "108.01 secs, 2139 evals, 2050 steps, improv/step: 0.240 (last = 0.1000), fitness=0.018611812\n",
      "108.54 secs, 2149 evals, 2060 steps, improv/step: 0.240 (last = 0.1000), fitness=0.018611812\n",
      "109.08 secs, 2160 evals, 2071 steps, improv/step: 0.239 (last = 0.0000), fitness=0.018611812\n",
      "109.62 secs, 2172 evals, 2083 steps, improv/step: 0.238 (last = 0.0833), fitness=0.018611812\n",
      "110.13 secs, 2183 evals, 2094 steps, improv/step: 0.237 (last = 0.1818), fitness=0.018611812\n",
      "110.63 secs, 2193 evals, 2104 steps, improv/step: 0.237 (last = 0.1000), fitness=0.018611812\n",
      "111.15 secs, 2204 evals, 2115 steps, improv/step: 0.236 (last = 0.1818), fitness=0.018611812\n",
      "111.66 secs, 2215 evals, 2126 steps, improv/step: 0.237 (last = 0.2727), fitness=0.018611812\n",
      "112.18 secs, 2226 evals, 2137 steps, improv/step: 0.236 (last = 0.0909), fitness=0.018611812\n",
      "112.73 secs, 2238 evals, 2149 steps, improv/step: 0.236 (last = 0.2500), fitness=0.018019478\n",
      "113.28 secs, 2250 evals, 2161 steps, improv/step: 0.237 (last = 0.4167), fitness=0.018019478\n",
      "113.81 secs, 2262 evals, 2173 steps, improv/step: 0.237 (last = 0.1667), fitness=0.018019478\n",
      "114.34 secs, 2273 evals, 2184 steps, improv/step: 0.236 (last = 0.1818), fitness=0.018019478\n",
      "114.87 secs, 2285 evals, 2196 steps, improv/step: 0.236 (last = 0.1667), fitness=0.018019478\n",
      "115.38 secs, 2296 evals, 2207 steps, improv/step: 0.237 (last = 0.3636), fitness=0.018019478\n",
      "115.88 secs, 2307 evals, 2218 steps, improv/step: 0.236 (last = 0.1818), fitness=0.018019478\n",
      "116.41 secs, 2319 evals, 2230 steps, improv/step: 0.235 (last = 0.0833), fitness=0.018019478\n",
      "116.93 secs, 2330 evals, 2241 steps, improv/step: 0.235 (last = 0.0909), fitness=0.018019478\n",
      "117.45 secs, 2342 evals, 2253 steps, improv/step: 0.235 (last = 0.2500), fitness=0.012552310\n",
      "117.96 secs, 2353 evals, 2264 steps, improv/step: 0.234 (last = 0.0909), fitness=0.012552310\n",
      "118.47 secs, 2364 evals, 2275 steps, improv/step: 0.235 (last = 0.3636), fitness=0.012552310\n",
      "119.00 secs, 2376 evals, 2287 steps, improv/step: 0.235 (last = 0.2500), fitness=0.012552310\n",
      "119.51 secs, 2387 evals, 2298 steps, improv/step: 0.234 (last = 0.0909), fitness=0.012552310\n",
      "120.03 secs, 2399 evals, 2310 steps, improv/step: 0.234 (last = 0.1667), fitness=0.012552310\n",
      "120.58 secs, 2411 evals, 2322 steps, improv/step: 0.233 (last = 0.0000), fitness=0.012552310\n",
      "121.11 secs, 2421 evals, 2332 steps, improv/step: 0.232 (last = 0.2000), fitness=0.012552310\n",
      "121.64 secs, 2433 evals, 2344 steps, improv/step: 0.232 (last = 0.0833), fitness=0.012552310\n",
      "122.17 secs, 2445 evals, 2356 steps, improv/step: 0.231 (last = 0.0833), fitness=0.012552310\n",
      "122.72 secs, 2454 evals, 2365 steps, improv/step: 0.230 (last = 0.1111), fitness=0.012552310\n",
      "123.24 secs, 2466 evals, 2377 steps, improv/step: 0.231 (last = 0.3333), fitness=0.012552310\n",
      "123.77 secs, 2477 evals, 2388 steps, improv/step: 0.230 (last = 0.0909), fitness=0.012552310\n",
      "124.32 secs, 2484 evals, 2395 steps, improv/step: 0.230 (last = 0.2857), fitness=0.012552310\n",
      "124.88 secs, 2496 evals, 2407 steps, improv/step: 0.230 (last = 0.1667), fitness=0.011625243\n",
      "125.40 secs, 2508 evals, 2419 steps, improv/step: 0.231 (last = 0.4167), fitness=0.011625243\n",
      "125.94 secs, 2518 evals, 2429 steps, improv/step: 0.231 (last = 0.2000), fitness=0.011625243\n",
      "126.47 secs, 2528 evals, 2439 steps, improv/step: 0.231 (last = 0.2000), fitness=0.011625243\n",
      "127.02 secs, 2539 evals, 2450 steps, improv/step: 0.230 (last = 0.0000), fitness=0.011625243\n",
      "127.58 secs, 2549 evals, 2460 steps, improv/step: 0.230 (last = 0.2000), fitness=0.011625243\n",
      "128.11 secs, 2561 evals, 2472 steps, improv/step: 0.229 (last = 0.1667), fitness=0.011625243\n",
      "128.66 secs, 2572 evals, 2483 steps, improv/step: 0.230 (last = 0.4545), fitness=0.011625243\n",
      "129.16 secs, 2583 evals, 2494 steps, improv/step: 0.231 (last = 0.2727), fitness=0.011625243\n",
      "129.67 secs, 2594 evals, 2505 steps, improv/step: 0.230 (last = 0.0909), fitness=0.011625243\n",
      "130.19 secs, 2604 evals, 2515 steps, improv/step: 0.229 (last = 0.1000), fitness=0.011625243\n",
      "130.73 secs, 2615 evals, 2526 steps, improv/step: 0.229 (last = 0.1818), fitness=0.011625243\n",
      "131.26 secs, 2627 evals, 2538 steps, improv/step: 0.229 (last = 0.2500), fitness=0.010072585\n",
      "131.80 secs, 2639 evals, 2550 steps, improv/step: 0.229 (last = 0.0833), fitness=0.010072585\n",
      "132.35 secs, 2650 evals, 2561 steps, improv/step: 0.228 (last = 0.1818), fitness=0.010072585\n",
      "132.85 secs, 2658 evals, 2569 steps, improv/step: 0.228 (last = 0.0000), fitness=0.010072585\n",
      "133.38 secs, 2670 evals, 2581 steps, improv/step: 0.227 (last = 0.1667), fitness=0.010072585\n",
      "133.91 secs, 2681 evals, 2592 steps, improv/step: 0.227 (last = 0.0909), fitness=0.010072585\n",
      "134.42 secs, 2693 evals, 2604 steps, improv/step: 0.227 (last = 0.1667), fitness=0.010072585\n",
      "134.94 secs, 2704 evals, 2615 steps, improv/step: 0.227 (last = 0.2727), fitness=0.010072585\n",
      "135.47 secs, 2716 evals, 2627 steps, improv/step: 0.226 (last = 0.1667), fitness=0.010072585\n",
      "135.99 secs, 2728 evals, 2640 steps, improv/step: 0.226 (last = 0.0769), fitness=0.010072585\n",
      "136.52 secs, 2739 evals, 2651 steps, improv/step: 0.226 (last = 0.1818), fitness=0.010072585\n",
      "137.14 secs, 2750 evals, 2662 steps, improv/step: 0.225 (last = 0.0909), fitness=0.010072585\n",
      "137.68 secs, 2761 evals, 2673 steps, improv/step: 0.226 (last = 0.4545), fitness=0.010072585\n",
      "138.23 secs, 2767 evals, 2679 steps, improv/step: 0.225 (last = 0.0000), fitness=0.010072585\n",
      "138.74 secs, 2778 evals, 2690 steps, improv/step: 0.226 (last = 0.2727), fitness=0.010072585\n",
      "139.28 secs, 2790 evals, 2702 steps, improv/step: 0.225 (last = 0.1667), fitness=0.009970221\n",
      "139.80 secs, 2801 evals, 2713 steps, improv/step: 0.226 (last = 0.2727), fitness=0.003726769\n",
      "140.36 secs, 2805 evals, 2717 steps, improv/step: 0.226 (last = 0.2500), fitness=0.003726769\n",
      "140.89 secs, 2811 evals, 2723 steps, improv/step: 0.225 (last = 0.1667), fitness=0.003726769\n",
      "141.41 secs, 2823 evals, 2735 steps, improv/step: 0.226 (last = 0.2500), fitness=0.003726769\n",
      "141.95 secs, 2835 evals, 2747 steps, improv/step: 0.225 (last = 0.1667), fitness=0.003726769\n",
      "142.48 secs, 2843 evals, 2755 steps, improv/step: 0.225 (last = 0.0000), fitness=0.003726769\n",
      "143.01 secs, 2855 evals, 2767 steps, improv/step: 0.225 (last = 0.3333), fitness=0.003726769\n",
      "143.52 secs, 2866 evals, 2778 steps, improv/step: 0.225 (last = 0.0909), fitness=0.003726769\n",
      "144.08 secs, 2875 evals, 2787 steps, improv/step: 0.224 (last = 0.1111), fitness=0.003726769\n",
      "144.61 secs, 2877 evals, 2789 steps, improv/step: 0.224 (last = 0.0000), fitness=0.003726769\n",
      "145.16 secs, 2880 evals, 2792 steps, improv/step: 0.224 (last = 0.0000), fitness=0.003726769\n",
      "145.72 secs, 2890 evals, 2802 steps, improv/step: 0.224 (last = 0.2000), fitness=0.003726769\n",
      "146.26 secs, 2899 evals, 2811 steps, improv/step: 0.223 (last = 0.1111), fitness=0.003726769\n",
      "146.78 secs, 2911 evals, 2823 steps, improv/step: 0.223 (last = 0.0833), fitness=0.003726769\n",
      "147.28 secs, 2922 evals, 2834 steps, improv/step: 0.223 (last = 0.2727), fitness=0.003726769\n",
      "147.82 secs, 2934 evals, 2846 steps, improv/step: 0.222 (last = 0.0833), fitness=0.003726769\n",
      "148.32 secs, 2945 evals, 2857 steps, improv/step: 0.222 (last = 0.0000), fitness=0.003726769\n",
      "148.86 secs, 2956 evals, 2868 steps, improv/step: 0.221 (last = 0.1818), fitness=0.003726769\n",
      "149.36 secs, 2966 evals, 2878 steps, improv/step: 0.221 (last = 0.1000), fitness=0.003726769\n",
      "149.90 secs, 2978 evals, 2890 steps, improv/step: 0.220 (last = 0.0833), fitness=0.003726769\n",
      "150.41 secs, 2989 evals, 2901 steps, improv/step: 0.220 (last = 0.0000), fitness=0.003726769\n",
      "150.95 secs, 3001 evals, 2913 steps, improv/step: 0.219 (last = 0.0000), fitness=0.003726769\n",
      "151.46 secs, 3012 evals, 2924 steps, improv/step: 0.219 (last = 0.2727), fitness=0.003726769\n",
      "151.97 secs, 3023 evals, 2935 steps, improv/step: 0.219 (last = 0.1818), fitness=0.003726769\n",
      "152.50 secs, 3035 evals, 2947 steps, improv/step: 0.218 (last = 0.0833), fitness=0.003726769\n",
      "153.03 secs, 3047 evals, 2959 steps, improv/step: 0.219 (last = 0.5000), fitness=0.003726769\n",
      "153.58 secs, 3058 evals, 2970 steps, improv/step: 0.219 (last = 0.0909), fitness=0.003726769\n",
      "154.12 secs, 3070 evals, 2982 steps, improv/step: 0.219 (last = 0.1667), fitness=0.003726769\n",
      "154.64 secs, 3081 evals, 2993 steps, improv/step: 0.219 (last = 0.1818), fitness=0.003726769\n",
      "155.17 secs, 3093 evals, 3005 steps, improv/step: 0.218 (last = 0.0000), fitness=0.003726769\n",
      "155.71 secs, 3105 evals, 3017 steps, improv/step: 0.217 (last = 0.1667), fitness=0.003726769\n",
      "156.22 secs, 3116 evals, 3028 steps, improv/step: 0.218 (last = 0.3636), fitness=0.003726769\n",
      "156.72 secs, 3127 evals, 3039 steps, improv/step: 0.218 (last = 0.1818), fitness=0.003726769\n",
      "157.23 secs, 3139 evals, 3051 steps, improv/step: 0.217 (last = 0.0000), fitness=0.003726769\n",
      "157.78 secs, 3151 evals, 3063 steps, improv/step: 0.217 (last = 0.3333), fitness=0.003726769\n",
      "158.30 secs, 3163 evals, 3075 steps, improv/step: 0.217 (last = 0.1667), fitness=0.003726769\n",
      "158.82 secs, 3174 evals, 3086 steps, improv/step: 0.217 (last = 0.2727), fitness=0.003726769\n",
      "159.35 secs, 3186 evals, 3098 steps, improv/step: 0.217 (last = 0.1667), fitness=0.003726769\n",
      "159.88 secs, 3198 evals, 3110 steps, improv/step: 0.217 (last = 0.2500), fitness=0.003726769\n",
      "160.40 secs, 3210 evals, 3122 steps, improv/step: 0.217 (last = 0.1667), fitness=0.003726769\n",
      "160.92 secs, 3221 evals, 3133 steps, improv/step: 0.217 (last = 0.1818), fitness=0.003726769\n",
      "161.43 secs, 3232 evals, 3144 steps, improv/step: 0.217 (last = 0.2727), fitness=0.003726769\n",
      "161.96 secs, 3244 evals, 3156 steps, improv/step: 0.217 (last = 0.0833), fitness=0.003726769\n",
      "162.48 secs, 3256 evals, 3168 steps, improv/step: 0.216 (last = 0.0000), fitness=0.003726769\n",
      "163.00 secs, 3268 evals, 3180 steps, improv/step: 0.216 (last = 0.2500), fitness=0.003726769\n",
      "163.50 secs, 3279 evals, 3191 steps, improv/step: 0.216 (last = 0.2727), fitness=0.003726769\n",
      "164.01 secs, 3290 evals, 3202 steps, improv/step: 0.216 (last = 0.1818), fitness=0.003726769\n",
      "164.54 secs, 3301 evals, 3213 steps, improv/step: 0.215 (last = 0.0000), fitness=0.003726769\n",
      "165.05 secs, 3312 evals, 3224 steps, improv/step: 0.215 (last = 0.0000), fitness=0.003726769\n",
      "165.59 secs, 3324 evals, 3236 steps, improv/step: 0.214 (last = 0.1667), fitness=0.003726769\n",
      "166.12 secs, 3333 evals, 3245 steps, improv/step: 0.214 (last = 0.1111), fitness=0.003726769\n",
      "166.63 secs, 3344 evals, 3256 steps, improv/step: 0.213 (last = 0.0000), fitness=0.003726769\n",
      "167.20 secs, 3350 evals, 3262 steps, improv/step: 0.213 (last = 0.1667), fitness=0.003726769\n",
      "167.72 secs, 3362 evals, 3274 steps, improv/step: 0.213 (last = 0.1667), fitness=0.003726769\n",
      "168.25 secs, 3374 evals, 3286 steps, improv/step: 0.213 (last = 0.1667), fitness=0.003726769\n",
      "168.78 secs, 3385 evals, 3297 steps, improv/step: 0.213 (last = 0.0909), fitness=0.003726769\n",
      "169.31 secs, 3396 evals, 3308 steps, improv/step: 0.213 (last = 0.3636), fitness=0.003726769\n",
      "169.83 secs, 3405 evals, 3317 steps, improv/step: 0.213 (last = 0.0000), fitness=0.003726769\n",
      "170.36 secs, 3414 evals, 3326 steps, improv/step: 0.213 (last = 0.2222), fitness=0.003726769\n",
      "170.88 secs, 3423 evals, 3335 steps, improv/step: 0.212 (last = 0.1111), fitness=0.003726769\n",
      "171.42 secs, 3435 evals, 3347 steps, improv/step: 0.212 (last = 0.0833), fitness=0.003726769\n",
      "171.93 secs, 3447 evals, 3359 steps, improv/step: 0.212 (last = 0.1667), fitness=0.003726769\n",
      "172.48 secs, 3459 evals, 3371 steps, improv/step: 0.212 (last = 0.1667), fitness=0.003726769\n",
      "173.01 secs, 3466 evals, 3378 steps, improv/step: 0.211 (last = 0.1429), fitness=0.003726769\n",
      "173.63 secs, 3470 evals, 3382 steps, improv/step: 0.211 (last = 0.2500), fitness=0.003726769\n",
      "174.15 secs, 3480 evals, 3392 steps, improv/step: 0.211 (last = 0.0000), fitness=0.003726769\n",
      "174.67 secs, 3491 evals, 3403 steps, improv/step: 0.211 (last = 0.2727), fitness=0.003726769\n",
      "175.19 secs, 3500 evals, 3412 steps, improv/step: 0.211 (last = 0.2222), fitness=0.003726769\n",
      "175.70 secs, 3510 evals, 3422 steps, improv/step: 0.211 (last = 0.2000), fitness=0.003726769\n",
      "176.20 secs, 3521 evals, 3433 steps, improv/step: 0.211 (last = 0.0909), fitness=0.003726769\n",
      "176.71 secs, 3530 evals, 3442 steps, improv/step: 0.210 (last = 0.1111), fitness=0.003726769\n",
      "177.22 secs, 3541 evals, 3453 steps, improv/step: 0.211 (last = 0.2727), fitness=0.003726769\n",
      "177.78 secs, 3551 evals, 3463 steps, improv/step: 0.210 (last = 0.0000), fitness=0.003726769\n",
      "178.29 secs, 3562 evals, 3474 steps, improv/step: 0.210 (last = 0.2727), fitness=0.003726769\n",
      "178.83 secs, 3574 evals, 3486 steps, improv/step: 0.210 (last = 0.0833), fitness=0.003726769\n",
      "179.35 secs, 3586 evals, 3498 steps, improv/step: 0.210 (last = 0.2500), fitness=0.003726769\n",
      "179.88 secs, 3598 evals, 3510 steps, improv/step: 0.210 (last = 0.1667), fitness=0.003726769\n",
      "180.41 secs, 3610 evals, 3522 steps, improv/step: 0.209 (last = 0.0000), fitness=0.003726769\n",
      "180.95 secs, 3619 evals, 3531 steps, improv/step: 0.208 (last = 0.0000), fitness=0.003726769\n",
      "181.50 secs, 3630 evals, 3542 steps, improv/step: 0.208 (last = 0.0909), fitness=0.003726769\n",
      "182.02 secs, 3642 evals, 3554 steps, improv/step: 0.208 (last = 0.1667), fitness=0.003726769\n",
      "182.56 secs, 3654 evals, 3566 steps, improv/step: 0.208 (last = 0.1667), fitness=0.003726769\n",
      "183.08 secs, 3666 evals, 3578 steps, improv/step: 0.208 (last = 0.1667), fitness=0.003726769\n",
      "183.59 secs, 3677 evals, 3589 steps, improv/step: 0.207 (last = 0.0909), fitness=0.003726769\n",
      "184.11 secs, 3689 evals, 3601 steps, improv/step: 0.207 (last = 0.1667), fitness=0.003726769\n",
      "184.62 secs, 3699 evals, 3611 steps, improv/step: 0.207 (last = 0.0000), fitness=0.003726769\n",
      "185.13 secs, 3710 evals, 3622 steps, improv/step: 0.206 (last = 0.0000), fitness=0.003726769\n",
      "185.65 secs, 3722 evals, 3634 steps, improv/step: 0.205 (last = 0.0000), fitness=0.003726769\n",
      "186.19 secs, 3734 evals, 3646 steps, improv/step: 0.205 (last = 0.1667), fitness=0.003726769\n",
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      "469.34 secs, 9676 evals, 9591 steps, improv/step: 0.160 (last = 0.0000), fitness=0.000033359\n",
      "469.85 secs, 9688 evals, 9603 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000033359\n",
      "470.35 secs, 9699 evals, 9614 steps, improv/step: 0.160 (last = 0.0000), fitness=0.000033359\n",
      "470.87 secs, 9711 evals, 9627 steps, improv/step: 0.160 (last = 0.0769), fitness=0.000033359\n",
      "471.40 secs, 9723 evals, 9639 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000033359\n",
      "471.91 secs, 9735 evals, 9651 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000033359\n",
      "472.46 secs, 9747 evals, 9663 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000033359\n",
      "472.99 secs, 9759 evals, 9675 steps, improv/step: 0.160 (last = 0.4167), fitness=0.000025629\n",
      "473.54 secs, 9771 evals, 9687 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000025629\n",
      "474.08 secs, 9783 evals, 9699 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000025629\n",
      "474.62 secs, 9795 evals, 9711 steps, improv/step: 0.160 (last = 0.3333), fitness=0.000025629\n",
      "475.15 secs, 9807 evals, 9723 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000025629\n",
      "475.65 secs, 9818 evals, 9734 steps, improv/step: 0.160 (last = 0.1818), fitness=0.000025629\n",
      "476.18 secs, 9830 evals, 9746 steps, improv/step: 0.160 (last = 0.3333), fitness=0.000025629\n",
      "476.71 secs, 9842 evals, 9758 steps, improv/step: 0.160 (last = 0.2500), fitness=0.000025629\n",
      "477.24 secs, 9854 evals, 9770 steps, improv/step: 0.160 (last = 0.0000), fitness=0.000025629\n",
      "477.80 secs, 9866 evals, 9782 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000025629\n",
      "478.31 secs, 9877 evals, 9793 steps, improv/step: 0.160 (last = 0.1818), fitness=0.000025629\n",
      "478.83 secs, 9889 evals, 9805 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000025629\n",
      "479.36 secs, 9900 evals, 9816 steps, improv/step: 0.160 (last = 0.1818), fitness=0.000025629\n",
      "479.86 secs, 9911 evals, 9827 steps, improv/step: 0.160 (last = 0.0909), fitness=0.000025629\n",
      "480.37 secs, 9922 evals, 9838 steps, improv/step: 0.160 (last = 0.2727), fitness=0.000025629\n",
      "480.87 secs, 9933 evals, 9849 steps, improv/step: 0.160 (last = 0.1818), fitness=0.000025629\n",
      "481.39 secs, 9945 evals, 9861 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000025629\n",
      "481.93 secs, 9957 evals, 9873 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000025629\n",
      "482.46 secs, 9969 evals, 9885 steps, improv/step: 0.160 (last = 0.3333), fitness=0.000025629\n",
      "482.97 secs, 9980 evals, 9896 steps, improv/step: 0.160 (last = 0.0909), fitness=0.000025629\n",
      "483.51 secs, 9992 evals, 9908 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000025629\n",
      "484.04 secs, 10004 evals, 9920 steps, improv/step: 0.160 (last = 0.0000), fitness=0.000025629\n",
      "484.58 secs, 10016 evals, 9932 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000025629\n",
      "485.09 secs, 10026 evals, 9942 steps, improv/step: 0.160 (last = 0.1000), fitness=0.000025629\n",
      "485.61 secs, 10038 evals, 9954 steps, improv/step: 0.160 (last = 0.0833), fitness=0.000025629\n",
      "486.12 secs, 10050 evals, 9966 steps, improv/step: 0.160 (last = 0.1667), fitness=0.000025629\n",
      "486.64 secs, 10061 evals, 9977 steps, improv/step: 0.160 (last = 0.0909), fitness=0.000023238\n",
      "487.17 secs, 10072 evals, 9988 steps, improv/step: 0.160 (last = 0.4545), fitness=0.000023238\n",
      "487.70 secs, 10083 evals, 9999 steps, improv/step: 0.160 (last = 0.0000), fitness=0.000023238\n",
      "\n",
      "Optimization stopped after 10001 steps and 487.83 seconds\n",
      "Termination reason: Max number of steps (10000) reached\n",
      "Steps per second = 20.50\n",
      "Function evals per second = 20.67\n",
      "Improvements/step = 0.16010\n",
      "Total function evaluations = 10085\n",
      "\n",
      "\n",
      "Best candidate found: [0.399998, 0.906487, 2.00825, 1.99975, 0.147545, 0.200009]\n",
      "\n",
      "Fitness: 0.000023238\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BlackBoxOptim.OptimizationResults(\"adaptive_de_rand_1_bin_radiuslimited\", \"Max number of steps (10000) reached\", 10001, 1.575471113486e9, 487.8269999027252, DictChain{Symbol,Any}[DictChain{Symbol,Any}[Dict{Symbol,Any}(:RngSeed => 342670,:SearchRange => Tuple{Float64,Float64}[(0.1, 1.0), (0.1, 1.0), (1.5, 4.0), (1.5, 4.0), (0.05, 0.2), (0.1, 0.25)],:MaxSteps => 10000),Dict{Symbol,Any}()],Dict{Symbol,Any}(:FitnessScheme => ScalarFitnessScheme{true}(),:NumDimensions => :NotSpecified,:PopulationSize => 50,:MaxTime => 0.0,:SearchRange => (-1.0, 1.0),:Method => :adaptive_de_rand_1_bin_radiuslimited,:MaxNumStepsWithoutFuncEvals => 100,:RngSeed => 1234,:MaxFuncEvals => 0,:SaveTrace => false…)], 10085, ScalarFitnessScheme{true}(), BlackBoxOptim.TopListArchiveOutput{Float64,Array{Float64,1}}(2.3238332610212827e-5, [0.39999813542572393, 0.9064870289371078, 2.00825093694817, 1.9997457371433491, 0.1475451964727831, 0.2000090042657465]), BlackBoxOptim.PopulationOptimizerOutput{FitPopulation{Float64}}(FitPopulation{Float64}([0.39999582023968416 0.40001800302277785 … 0.40001221274438725 0.40001800864570025; 0.9078550844700933 0.9091649276507853 … 0.9114258086692654 0.9095210926662284; … ; 0.14715018424724421 0.14654537262428868 … 0.14584056186934855 0.1463809761718443; 0.19999804049034417 0.19995705152087756 … 0.199963412237228 0.19994596155104924], NaN, [5.315979445569093e-5, 8.764278374106651e-5, 8.832596520856464e-5, 6.901331317996151e-5, 8.242978158802385e-5, 7.70125950469246e-5, 8.15601464586419e-5, 6.598260803961049e-5, 7.628067381300808e-5, 9.448251633839513e-5  …  5.673979268488549e-5, 7.22925623979078e-5, 9.544172868589259e-5, 7.026649046163648e-5, 5.9152649497055956e-5, 5.372443810614412e-5, 5.30295787749722e-5, 0.00010297374370435685, 0.00010501626560904943, 0.00011227855411683369], 0, BlackBoxOptim.Candidate{Float64}[BlackBoxOptim.Candidate{Float64}([0.40001666207095155, 0.9045613269369748, 2.005576804298473, 2.0000691400643276, 0.14831257133000528, 0.1999930266209056], 33, 4.1716694147443185e-5, BlackBoxOptim.AdaptiveDiffEvoRandBin{3}(BlackBoxOptim.AdaptiveDiffEvoParameters(BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.65, σ=0.1), Distributions.Cauchy{Float64}(μ=1.0, σ=0.1), 0.5, false, true), BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.1, σ=0.1), Distributions.Cauchy{Float64}(μ=0.95, σ=0.1), 0.5, false, true), [0.960674322251959, 0.9581303909794152, 0.8522871247779139, 0.7610893640593427, 0.659582004449304, 0.48178863349694556, 0.6950846231523309, 0.8704360788207649, 0.9219643404375019, 1.0  …  1.0, 0.9386474095440099, 0.9543424300003557, 0.43907300833940044, 0.6394905172733162, 1.0, 1.0, 0.5199196870001682, 0.9649232696695624, 0.9123915541318597], [1.0, 1.0, 0.8522356164335076, 1.0, 1.0, 0.7120976372546113, 0.12389854064189035, 1.0, 0.17941474174929367, 0.9853649515873368  …  0.6567290434385484, 0.023304418985069034, 0.9341063811312418, 0.7962778660507044, 1.0, 1.0, 0.0138724141191999, 0.774083152274134, 0.07115539976214924, 1.0])), 0), BlackBoxOptim.Candidate{Float64}([0.4000163051337856, 0.9045613269369748, 2.005231804867753, 2.0000691400643276, 0.14831257133000528, 0.20000265157474725], 33, 7.56482239289737e-5, BlackBoxOptim.AdaptiveDiffEvoRandBin{3}(BlackBoxOptim.AdaptiveDiffEvoParameters(BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.65, σ=0.1), Distributions.Cauchy{Float64}(μ=1.0, σ=0.1), 0.5, false, true), BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.1, σ=0.1), Distributions.Cauchy{Float64}(μ=0.95, σ=0.1), 0.5, false, true), [0.960674322251959, 0.9581303909794152, 0.8522871247779139, 0.7610893640593427, 0.659582004449304, 0.48178863349694556, 0.6950846231523309, 0.8704360788207649, 0.9219643404375019, 1.0  …  1.0, 0.9386474095440099, 0.9543424300003557, 0.43907300833940044, 0.6394905172733162, 1.0, 1.0, 0.5199196870001682, 0.9649232696695624, 0.9123915541318597], [1.0, 1.0, 0.8522356164335076, 1.0, 1.0, 0.7120976372546113, 0.12389854064189035, 1.0, 0.17941474174929367, 0.9853649515873368  …  0.6567290434385484, 0.023304418985069034, 0.9341063811312418, 0.7962778660507044, 1.0, 1.0, 0.0138724141191999, 0.774083152274134, 0.07115539976214924, 1.0])), 0)])))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_init = [0.9, 0.8, 2.5, 2.5, 0.1, 0.1]\n",
    "x_lb = [0.1, 0.1, 1.5, 1.5, 0.05, 0.1]\n",
    "x_ub = [1.0, 1.0, 4.0, 4.0, core_flood.initial_water_saturation, 0.25]\n",
    "\n",
    "res = bboptimize(f, SearchRange = [(0.1, 1.0), (0.1, 1.0), (1.5, 4.0), (1.5, 4.0), \n",
    "        (0.05, core_flood.initial_water_saturation), (0.1, 0.25)])\n",
    " \n",
    "#    NumDimensions = 6, Method = :de_rand_1_bin)\n",
    "\n",
    "# opt_alg=:LD_SLSQP\n",
    "# opt1 = Opt(opt_alg, length(x_init)) # choose the algorithm\n",
    "# lower_bounds!(opt1, x_lb)\n",
    "# upper_bounds!(opt1, x_ub)\n",
    "# ftol_rel!(opt1, 1e-15)\n",
    "# ftol_abs!(opt1, 1e-15)\n",
    "\n",
    "# min_objective!(opt1, obj_fun)\n",
    "# (fObjOpt, paramOpt, flag) = optimize(opt1, x_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6-element Array{Float64,1}:\n",
       " 0.39999813542572393\n",
       " 0.9064870289371078 \n",
       " 2.00825093694817   \n",
       " 1.9997457371433491 \n",
       " 0.1475451964727831 \n",
       " 0.2000090042657465 "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_candidate(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9lxYtWnDZZZfx2WefuXy+goIChg0bRvPmzUlISCArK4tDhw65XHfixIk8+uijtG7dmsTERJ5++mnn+eqBjdtvvx2TyeQXAx0+DU379+/nF7/4BZ07d+aOO+4gIiKC/Px8UlJSAHj00UeZNGkSDzzwAH369OGHH35g2bJltGjRwnmNRYsW0b17dwYNGsSgQYPo0aMH7733nvO8xWJh+fLl7N+/nz59+vDAAw+QnZ1NdnZ2o3/egJWSAeYkjP1O3DGBuZ3RzhO1rRpe7cNx8FlOowydi0gQqO96zQYyYMAAJk2aRFZWFuXl5Xz77bc8/vjjzJ8/n7Zt2zrbvfvuu4SFhbFmzRrmzp3Lyy+/zFtvveU8f++99/L111+zePFi/v3vf/Pzn/+cIUOGsHPnTmeb48ePM336dN566y22bt3q9uaq6veKi4tj7dq1PPTQQ/zmN7/h5z//ORkZGWzcuJHBgweTlZXF8eNGDb/i4mL69+/PVVddxfr168nLy+PAgQOMHDmyxnVjYmJYs2YNf/zjH3n22WedM0zr1q0D4J133qG4uNj53JdMDpXGvihv75IckKr+b8wBmFz+A1MVpEYuNMJQra+ZdfF27piTjHIGtX1PEWkyLrS7vUf2/MuYiruYsR9D6nW1v/4FjBs3jvfff79Gv3NycnjiiSc4deoU/fr1o1OnTmzdupX09HTmz5/vbDdgwABKS0vZunWrc9bkscceIzc3l4KCAnbt2kWnTp3Yv38/SUlJztcNHDiQvn378vzzz7NgwQLuvfdeNm3axJVXXunStyNHjjjXEg0YMACbzeYc+bLZbFgsFu644w4WLlwIGOuP27Zty+rVq+nXrx9PPvkka9as4e9//7vzuvv37yc5OZnt27dz+eWX17guQN++fbnxxhuZMWMGYKxpWrp06QUXpl/o34G3v799XtxSAkRaJoxcyIncKUSfPGvtkjkJhsyoW3ipLkfw0b3GFFxtWIuMwHXXu8ZaKRGR2vLWes06uuGGG3jttddcjrVu3RqAiIgI3n//fXr06EFKSgqzZ8+u8fp+/fq5LDNJT09n5syZ2Gw2Nm7ciMPh4PLLL3d5TWVlJbGxPy2PiIiIoEePHhft69ltQkNDiY2NpXv37s5j1TdWVd9otWHDBj7//HOaN6+51dauXbuc/Tr3vdu2beu8hj9SaBLPpWXy6r7LWP/lp9zRKYyRA/oYU3IhoXW/ZrfbjMGq/x1bt9d/dK/x+q4KTiJSS95cr1kHMTExdOzY8bznq4s0Hz58mMOHDxMTc55tq9yw2+2EhoayYcMGQkNd/xt9dpCJioryaH1veHi4y3OTyeRyrPoadrvd+fOWW27hhRdqFjg+e4rR3XWrr+GPFJqkVvaWnSLfnsZNl3WB1A7euWjX28BRxxGn6kXiDo04iUgtVa/XtBbjfl2TyTjv6XpNL9q1axePPPII8+fPZ8mSJYwZM4YVK1YQEvLTUuT8/HyX1+Tn59OpUydCQ0Pp2bMnNpuN0tJSrrvOu1OLnujVqxcfffQRl156KWFhdY8a4eHh2Gz+s4bVr0oOiP8rPGws8ktuHeXdC9e1HEE1LRIXkdpy2erp3NGWqudDZtRvNP0CKisrKSkpcXkcOnQIm81GVlYWgwYN4t577+Wdd95hy5YtzJw50+X1hYWFZGdns337dj744ANeeeUVHn74YQAuv/xyRo8ezZgxY/jLX/7Cnj17WLduHS+88AKffvppg3yes02YMIHDhw/zi1/8grVr17J7926WLVvGf/3Xf9UqBF166aWsWLGCkpISysrKGrDHnlFoklr5KTRFe//idSmA6eSANa83Wn0VEWkiqtZrYm7retycVLcbXGohLy+Ptm3bujyuvfZannvuOb7//nvefPNNwCjS/NZbb/G73/2OTZs2OV8/ZswYTpw4Qd++fZkwYQIPPfQQv/rVr5zn33nnHcaMGcPkyZPp3LkzmZmZrFmzplG2BktKSuLrr7/GZrMxePBgunXrxsMPP4zFYnEZLbuYmTNnsnz5cpKTk+nZs2cD9tgzunvOA7p7znCs8gxdnzLuhPj304MwNwu/yCvqaOtf677G6WxaJC7SpNX77rmz2W3GrgYVB4w1TPVdr9nABgwYwFVXXeV2gXiwacy75zTSJB4rLDNGmVpGhzdcYIKfCmCaky7e9kI+HAefT9d0nYhcXEioUVag+13GTz8OTOI7Ck3isX0/GqGpfUNMzZ0rLRMmbTHqo1x9f92n7FbOgOnJ8MULCk8iIlIvuntOPGO3cXrXl2SGfEuHyMvAnt7w/ydW/X9+qddB+/S6T9mdPgZfPG+sebpljgpiikjAO3s7FGk8GmmSi6vazHL4xvHMjZjHpB8eafzF1vVaJF7lxGGjIKZGnUREpA4UmuTCfLiZZQ31LUtQ7Yvn4cUOCk8iTYTuZwpujfn3r9Ak5+ePm1l6a5H4iSNGeNJ6J5GAVV1NunqTWAlOp06dAqhR+bwhaE2TnN/eVTVHmFw4wPqD0c7Lm1leUFomXDHceN//fAxr36x9JfFq1eudvp4D1zwM10/RXTMiASI0NJSWLVs69yqLjo72aEsQaTrsdjsHDx4kOjq6XpXHPaXQJOfn480sL8hbi8SrVYenVa9AryzoPMzv67SIiFH4EfDrTV6lYYWEhNC+fftGCcwKTXJ+Pt7M0mNdbwPTe/C3iXCinmX2Tx2F/FeNR1RLuPoBjT6J+DGTyUTbtm2Jj4/n9OnTvu6O+EBEREStqozXhyqCeyBoK4LbbcZdchfbzHLSZv8IFXYbfPkSrJoDp45577rhMZq6ExEJQKoILo3nrM0sa0amht/MstZCQmFADjxWCAOmQbOW3rlu9dSdFo2LiAQ1jTR5IGhHmqoV5HL4o0dobTv00zFzOyMw+XOhyOqRpy+e9+51I1po3ZOISADw9ve3QpMHgj40ATe9uII2ZRt59sZYLr+sU2CFhYJc76x3ckfrnkRE/Jam56TRnbbZ+b6sknx7GuafjQq8zSzTMuG3u4wpu4gY715b9Z5ERIKG7p6Tiyo8fByb3UFUeCgJ5khfd6duqtc7XT+lYRaLn12yoOdoaNkeYtpAi7aBNSonIiLnpdAkF7X7oBEuUuNiAr9w3Lnhac1r3p22O3XU2Bj4bJrCExFpEjQ9Jxe155ARmjq08fLUli9Vh6ff7oKxH0O/B4wF3g1BU3giIk2CRprkwuw2HN//i8yQHVwb3hXsVzat0ZKzK4sP+kPDTN1V0xSeiEhA091zHgjau+cKco0Ne8/ef86cZNRu8udSA/XVUEUyL0RTeCIiXqeSAz4QlKGpIBeWjKFmWcuqNU0jFzbt4AQ/hSdvr3u6kPAYyHjIGHk6dtDYokajUCIidaLQ5ANBF5qc26cUnaeBn22f0tDsNti7CrZ/ChvfMxZ7NyaNQomI1IlCkw8EXWja8y94d8TF24392FgLFEx8MXVXLSwaut4Olw3QOigREQ94+/tbC8GlpooD3m3XlDR0yYILOXMcvl1kPACiY6H7z7WgXESkkSg0SU3NE7zbrik6OzxVT939ewkcP3Tx13rL8R9r1oRqZoHOIzQaJSLSADQ954Ggm55zrmkqpuZCcAi6NU2eql77dLQYdn8OBf/X+FN459JolIgEMa1p8oGgC03gvHvOjuOcCqhBdPdcffni7jtPRLWEvvfrDj0RafIUmnwgKEMT4Cj4Pw78+RESTT/+dNDcDobMUGCqjbPvvmvsKTxPaVpPRJoghSYfCNbQdPBoJVc/t4y+If/hvZEphFv0ZVpv/jiF545Go0SkCdDdc9Jodh+swE4IP7TsTfhVN/q6O01D9bYtAD1GQuY8/5zCO3EEVs5wPVY9GtXhemMRutZIiUiQUWiS86reqDc1rrmPe9KEnXsXXsUBiI6Dfath9Sv+NQp1sty15EE1LTYXkSCh0CTnVR2aOsTF+LgnQeDsESgw1hb1f9Q/R6HOdbHSBzHxYDJpmk9EAp5Ck5zX7urQ1EahySfOHYU6WmwEjyOFsPl//XNBebXzjUqBApWIBCyFJjmvn6bnFJp86txRKIDBz7kuKN/+mX+PRp3Nk0B19ropBSsR8RMKTVKT3YZtz9d0P7yMuJCWpLa+3tc9knOdu6D87LvyAmU0yp0LBapqWpAuIj6ikgMeCKqSAwW5kJcD1iLnIYc5CdOQF1SbKdCcW94gkEaj6uLsBenRsUagqv6pYCUSlFSnyQeCJjRVVQGvuXWKqoA3CdUh6uw79Na+0bSD1LncBStNAYo0WQpNPhAUocm531zReRpov7kmKdhGozyltVUiTYJCkw8ERWja8y94d8TF2439uOaiZGk6NBrluQsFq4oDRrjStKCIT6kiuDSMigPebSeB6Xz1os5eZF49rRWoi829xZNF69XclVmoDlZadyUSMBSaxNA8wbvtpOlwV/KgWiCXPmhMtQlYF1vQrmlCEZ9RaBJDSoaxZslaTM2F4OBc05SS0dg9E392sdIH1V/ymubznLsK6xfiyTShRrNEvMJv1jRNnz6dadOm8fDDDzN79mwABgwYwMqVK13a3X333SxevNj5vKysjIkTJ5KbmwtAZmYmr7zyCi1btnS22bx5Mw8++CBr166ldevW/PrXv+aJJ57AZDJ51LegWNMEzrvnHIDJJTjp7jnxAgUq/xHVEvreb4Qnd8FKoUuaiCa5pmndunW8+eab9OjRo8a58ePH8+yzzzqfR0VFuZwfNWoU+/fvJy8vD4Bf/epXZGVl8be//Q0w/sBuvvlmbrjhBtatW8eOHTsYN24cMTExTJ48uQE/VQBKy4SRCzn18W+JPF7y03FzEgyZocAk9XO+ab4LrZtSsGoYJ47Ayhl1f/2FQteFwpdClwQ4n4emiooKRo8ezfz58/nDH/5Q43x0dDSJiYluX7tt2zby8vLIz8/n6quvBmD+/Pmkp6ezfft2OnfuzKJFizh58iQLFiwgMjKSbt26sWPHDmbNmkV2drbHo01BIy2Tvx2/kg//soTrEm1MuOUa/QdOGt6F1k2BFqT7m/qGLk8XxmvES/yMz0PThAkTGD58OAMHDnQbmhYtWsT7779PQkICQ4cO5amnnqJFixYArF69GovF4gxMAP369cNisbBq1So6d+7M6tWr6d+/P5GRkc42gwcPZurUqXz//fekpqbWeM/KykoqKyudz61Wqzc/st8rLKsk355G6iXJkFpz9E/EJzxdkO5u1EPByr/UZmH8hdR1xEsL7KWOfBqaFi9ezIYNG1i/fr3b86NHjyY1NZXExES2bNnC1KlT+fbbb1m+fDkAJSUlxMfH13hdfHw8JSUlzjaXXnqpy/mEhATnOXehafr06TzzzDP1+WgBrfDwcQCSW0f7uCciHrrYSBWcP1hpCjBw1XfE60Jqu8Dek2Cm0bGA57PQVFhYyMMPP8yyZcto1qyZ2zbjx493/t6tWzc6depEnz592LhxI7169QJwO73mcDhcjp/bpnrt+/mm5qZOnUp2drbzudVqJTk52cNPFvj2VYemVgpN0oTUZwpQwSr4eGs07FyelpSoy8iZglmD81lo2rBhA6WlpfTu3dt5zGaz8eWXXzJv3jwqKysJDXX9S+/Vqxfh4eHs3LmTXr16kZiYyIEDNYstHjx40DmalJiY6Bx1qlZaWgr8NOJ0rsjISJfpvGBTWGaEpvYaaZJg441gVf1lprpV4k5tS0rURUMGsyCf0vRZaLrpppvYvHmzy7F7772XK664gpycnBqBCWDr1q2cPn2atm3bApCenk55eTlr166lb9++AKxZs4by8nIyMjKcbaZNm8apU6eIiIgAYNmyZSQlJdWYthM4edrGAauxnkuhScQNT6YC4cJ1q9x9GWndlXhLYwQzdzxZ4B/gd1f6TZ0mMOoyXXXVVcyePZtdu3axaNEihg0bRlxcHAUFBUyePJmoqCjWrVvnDFVDhw6lqKiIN954AzBKDqSkpDhLDpSXl9O5c2duvPFGpk2bxs6dOxk3bhxPPvmkxyUHgqZOE/Bd6VEGzvqS5pFhbH56kO4uFGlM54as833RaJpQmjJzEgx5wStlbppknSZ3IiIiWLFiBXPmzKGiooLk5GSGDx/OU0895TIKtWjRIiZOnNeDIVUAACAASURBVMigQYMAo7jlvHnznOctFgvLly9nwoQJ9OnTh1atWpGdne2yZkmq2G0c3fYFmSH5NGuRhMkxEEz+mfZFmiRPR7FqM01YfW7PSk0XSmCwFsGSMX5ZUNmvRpr8VVCMNBXkQl6O8Y+1mhfTvoj4geqRrIoDEB1XuzvBFLqkUVVt3TVpc72m6rz9/a3Q5IEmH5qqtk+pueectk8RkbN4Erq0MF68aezHno2+nkfQTM9JI7HbjBEmt5v0GrvQkfcYXDHcbxfmiUgj8XT60J3aLozXiJeA8XfvRxSagt3eVa5TcjU4wPqD0a4eaV9EpF6hq9qV99RvxEsL7ANLc/elgXxFoSnYeZri/Szti0gQ80b4cqcuC+w9DWYqKVFLVWuaUjJ83REXCk3BztMU72dpX0SkQTRUIIOL75FY38KTTS2YDZnhd8tCFJqCXUqGkeatxbhf1+SfaV9EJOA0ZCCr1tDBrDGmNM3tjMDkhzcgKTQFu5BQo6zAkjE4MGFyCU5Vd8/5YdoXERE3GiOYnc3dlGZdg1kAVARXaBIjzY9ciHXpZCynS386bk7y27QvIiJ+orGDmg8pNIkhLZNH8ttw/LuveOhnLbjmqq5+nfZFREQaW4ivOyD+Y9ePJ8m3pxHS4+fG/zUoMImIiDgpNAkAp87YKTx8HIAObWJ83BsRERH/o9AkAOw7fBy7A2IiQolvEenr7oiIiPgdhSYBYM+hYwCktonBZDL5uDciIiL+R6FJwG7j5M4vyAxZxc3RO40tCkRERMSFyeFwuKtoKGfx9i7JfqUg19iw9+z958xJRu0mlRoQEZEA5u3vb400BbOCXFgypuaGvdZi43hBrm/6JSIi4ocUmoKV3WaMMLndOqXqWN5jmqoTERGpotAUrPauqjnC5MIB1h+MdiIiIqLQFLQqDni3nYiISBOn0BSsmid4t52IiEgTp9AUrFIyjLvkOF9NJhOY2xntRERERKEpaIWEGmUFcLcUvCpIDZmh/edERESqKDQFs7RMGLmQQyFxrsfNSTByoeo0iYiInCXM1x0Q33J0uYUbz4TR9cxWZg9PJDHpUmNKTiNMIiIiLhSagtzBo5UcPeVgrSmN1lcPhTANPoqIiLijb8ggt7tqo97k1tFEKDCJiIicl74lg9yeqtCUGhfj456IiIj4N4WmILf7YAWg0CQiInIxWtMUzOw2Igq/JjPke9JDjoP9Ci0AFxEROQ+FpmBVkAt5OfzWWgQRwDpg+5NG7SaVGhAREalB03PBqCAXlozBce6GvdZiWDLGOC8iIiIuFJqCjd0GeTmAw80GKlW1wfMeM9qJiIiIk0JTsNm7Cs4dYXLhAOsPRjsRERFxUmgKNhUHvNtOREQkSCg0BZvmCd5tJyIiEiQUmoJNSoaxIa+bFU0GE5jbGe1ERETESaEp2ISEGmUFAHuNk1VBasgM1WsSERE5h0JTMErLxDHyXQ44WrseNyfByIWq0yQiIuKGilsGqUPJQ7imci59Q/7DeyNTCLe0NabkNMIkIiLilkJTkNp3+Dh2QtjXohfhV93k6+6IiIj4PU3PBan9ZccBSG4d7eOeiIiIBAaFpiC170eFJhERkdpQaApS+w4boam9QpOIiIhHFJqCVGGZQpOIiEhtKDQFqcLDJwBIbh3l456IiIgEBt09F2zsNk7v/preR1eQHNKS5JYDfN0jERGRgKDQFEwKciEvh3BrEXPDjUOOt//bqBCugpYiIiIX5DfTc9OnT8dkMjFp0iTnscrKSh566CHi4uKIiYkhMzOT/fv3u7xu37593HLLLcTExBAXF8fEiRM5deqUS5uVK1fSu3dvmjVrRocOHXj99dcb5TP5lYJcWDIGrEUuh03WYuN4Qa6POiYiIhIY/CI0rVu3jjfffJMePXq4HJ80aRJLly5l8eLFfPXVV1RUVDBixAhsNhsANpuN4cOHc+zYMb766isWL17MRx99xOTJk53X2LNnD8OGDeO6667jm2++Ydq0aUycOJGPPvqoUT+jT9ltkJcDONycrDqW95jRTkRERNzyeWiqqKhg9OjRzJ8/n1atWjmPl5eX8/bbbzNz5kwGDhxIz549ef/999m8eTP/+Mc/AFi2bBkFBQW8//779OzZk4EDBzJz5kzmz5+P1WoF4PXXX6d9+/bMnj2bLl268Mtf/pL/+q//4qWXXvLJ5/WJvatqjDC5coD1B6OdiIiIuOXz0DRhwgSGDx/OwIEDXY5v2LCB06dPM2jQIOexpKQkunXrxqpVxpf76tWr6datG0lJSc42gwcPprKykg0bNjjbnH2N6jbr16/n9OnTbvtUWVmJ1Wp1eQS0igPebSciIhKEfBqaFi9ezIYNG5g+fXqNcyUlJURERLiMPgEkJCRQUlLibJOQkOByvlWrVkRERFywTUJCAmfOnOHQoUNu+zV9+nQsFovzkZycXOfP6BeaJ1y8TW3aiYiIBCGfhabCwkIefvhhFi1aRLNmzTx+ncPhwGQyOZ+f/bunbRwOx3lfCzB16lTKy8udj8LCQo/755dSMsCcBLj/vGACczujnYiIiLjls9C0YcMGSktL6d27N2FhYYSFhbFy5Urmzp1LWFgYCQkJnDp1irKyMpfXlZaWOkeOEhMTnSNK1crKyjh9+vQF25SWlhIWFkZsbKzbvkVGRmI2m10eAS0k1CgrADhqBKeq50NmGO1ERETELZ+FpptuuonNmzezadMm56NPnz6MHj3a+Xt4eDjLly93vqa4uJgtW7aQkWGMiKSnp7NlyxaKi4udbZYtW0ZkZCS9e/d2tjn7GtVtqq8fNNIyYeRCTkWfMwVnToKRC1WnSURE5CJ8VtyyRYsWdOvWzeVYTEwMsbGxzuP33XcfkydPJjY2ltatWzNlyhS6d+/uXDQ+aNAg0tLSyMrK4sUXX+Tw4cNMmTKF8ePHO0eH7r//fubNm0d2djbjx49n9erVvP3223zwwQeN+4H9QVomS4505ZOP/8KN7Rz8ali6MSWnESYREZGL8uuK4C+//DJhYWGMHDmSEydOcNNNN7FgwQJCQ40v+dDQUD755BMeeOABrrnmGqKiohg1apRLOYHU1FQ+/fRTHnnkEf70pz+RlJTE3LlzufPOO331sXxq16GT5NvTuDK1A6R28XV3REREAobJUb0qWs7LarVisVgoLy8P+PVNY/57LV/uOMiMO7pzT9/2vu6OiIhIg/H297fP6zRJ49pzqAKA1LgYH/dEREQksCg0BZHKMzb2l50AILWNQpOIiEhtKDQFkb0/HsfhgBaRYbRpHunr7oiIiAQUv14ILl5kt3Fk6z/JDFlLc0s7TA47mHTXnIiIiKcUmoJBQS7k5dDXWkTfCMAKzH7FKHip+kwiIiIe0fRcU1eQC0vGgLXI9bi12DhekOubfomIiAQYj0aarFZrrS8c6LfmNwl2G+TlAO6qSjgAE+Q9BlcMV4FLERGRi/AoNLVs2fK8m9u6YzKZ2LFjBx06dKhzx8QL9q6qOcLkwgHWH4x2qdc1WrdEREQCkcdrmj788ENat2590XYOh4Nhw4bVq1PiJRUHvNtOREQkiHkUmlJSUrj++uuJjY316KIdOnQIrs1w/VXzhIu3qU07ERGRIOZRaNqzZ0+tLrply5Y6dUa8LCUDzEnGom+365pMxvmUjMbumYiISMDR3XNNWUioUVYAd5Gpao3akBlaBC4iIuKBOtVpOnbsGCtXrmTfvn2cOnXK5dzEiRO90jHxkrRMGLmQI3/JptWZgz8dNycZgUl1mkRERDxS69D0zTffMGzYMI4fP86xY8do3bo1hw4dIjo6mvj4eIUmf5SWSdYyM81L1/H49a3p3vlyY0pOI0wiIiIeq/X03COPPMItt9zC4cOHiYqKIj8/n71799K7d29eeumlhuij1JPD4WDXjyfJt6cR3ftuo7yAApOIiEit1Do0bdq0icmTJxMaGkpoaCiVlZUkJyfzxz/+kWnTpjVEH6WeDlgrOXHaRmiIifato33dHRERkYBU69AUHh7uLHSZkJDAvn37ALBYLM7fxb/sPlgBQPvW0YSHau2/iIhIXdR6TVPPnj1Zv349l19+OTfccANPPvkkhw4d4r333qN79+4N0Uepp92HjgGQGhfj456IiIgErloPOzz//PO0bdsWgN///vfExsbym9/8htLSUt58802vd1DqyW7DvvtfZIasYkDEdmM/OhEREak1k8PhcFf1UM5itVqxWCyUl5cH1kbEBbnGhr1n7z9nTjJqN6nUgIiINHHe/v6u1fTcmjVryM3N5fTp0wwcOJBBgwbVuwPSQApyYckYapS1tBYbx0cuVHASERGpBY+n55YuXco111zDnDlzePPNNxk6dCizZ89uyL5JXdltxgiT261Tqo7lPaapOhERkVrwODQ9//zzjBs3jiNHjnDkyBGeeeYZ/vCHPzRk36Su9q5ynZKrwQHWH4x2IiIi4hGPQ9P27dt59NFHCQszZvR++9vfcuTIEQ4dOtRgnZM6qjjg3XYiIiLieWiqqKigZcuWzueRkZFERUVhtVobpGNSD80TvNtOREREarcQ/O9//zsWi8X53G63s2LFCrZs2eI8lpmpxcU+l5Jh3CVnLcb9uiaTcT4lo7F7JiIiErA8LjkQEnLxQSmTyYTN1vQWFwdkyYGqu+fsOM4ZTjSquevuORERaeq8/f3t8fSc3W6/6KMpBqaAlZYJIxdykFjX4+YkBSYREZE6qPU2KhI4Ki4bRvpJE31D/sN/35lMdOt2xpRcSKivuyYiIhJwPBppqi5o6alPP/2UEydO1LlT4h17Dh7DTgjfRV9FdO97IPU6BSYREZE68ig03X777Rw5csTji95zzz0UFxfXuVPiHbsPVQDaqFdERMQbPJqeczgcjBs3jsjISI8uevLkyXp1Srxjz6FjAHSIa+7jnoiIiAQ+j0LT2LFja3XR0aNHB85dZk1YdWhKbaORJhERkfryKDS98847Dd0P8Ta7jZii1WSG/EAv+xmwX6r1TCIiIvWgu+eaooJcHHk5PG8tgghgJfBNEgx5QaUGRERE6sjjOk0SIKqKWtbYsNdabBwvyPVNv0RERAKcQlNTYrdBXg7gqK77fZaqwu95jxntREREpFYUmpqSvatqjjC5cID1B6OdiIiI1IpCU1NSccC77URERMSpTqFpxYoVjBgxgssuu4yOHTsyYsQI/vGPf3i7b1JbzRO8205EREScah2a5s2bx5AhQ2jRogUPP/wwEydOxGw2M2zYMObNm9cQfRRPpWQYG/K6WdFkMIG5av85ERERqRWTw+Fw1OYF7dq1Y+rUqTz44IMux//0pz/x3HPPUVR0oTU1gclqtWKxWCgvL/f/op1Vd8/ZcZyTiKuC1MiFKjsgIiJBwdvf37UeabJarQwZMqTG8UGDBmG1WuvdIamntExO3P4OJY7WrsfNSQpMIiIi9VDr4paZmZksXbqU3/72ty7H/+///o9bbrnFax2TutvT5iZGVM7lpujvmH97srGGKSVDFcFFRETqodahqUuXLjz33HN88cUXpKenA5Cfn8/XX3/N5MmTmTt3rrPtxIkTvddT8di+w8exE0JpbF/ofo2vuyMiItIk1HpNU2pqqmcXNpnYvXt3nTrlbwJqTRMw/8vdPPfpNkb0aMu8Ub183R0RERGf8Pb3d61Hmvbs2VPvN5WGVVh2HID2raN93BMREZGmw6fFLV977TV69OiB2WzGbDaTnp7OZ5995jw/YMAATCaTy+Oee+5xuUZZWRlZWVlYLBYsFgtZWVkcOXLEpc3mzZvp378/UVFRtGvXjmeffZZaDrAFlH2HFZpERES8zaORpuzsbI8vOGvWLI/bXnLJJcyYMYOOHTsC8O6773LrrbfyzTff0LVrVwDGjx/Ps88+63xNVFSUyzVGjRrF/v37ycvLA+BXv/oVWVlZ/O1vfwOMobmbb76ZG264gXXr1rFjxw7GjRtHTEwMkydP9rivgaSwKjQlKzSJiIh4jUeh6ZtvvnF5vmHDBmw2G507dwZgx44dhIaG0rt371q9+bl32z333HO89tpr5OfnO0NTdHQ0iYmJbl+/bds28vLyyM/P5+qrrwZg/vz5pKens337djp37syiRYs4efIkCxYsIDIykm7durFjxw5mzZpFdnY2JtP5CkEGJrvdQWHZCUAjTSIiIt7k0fTc559/7nzccsstDBgwgP3797Nx40Y2btxIYWEhN9xwA8OHD69zR2w2G4sXL+bYsWPOu/IAFi1aRFxcHF27dmXKlCkcPXrUeW716tVYLBZnYALo168fFouFVatWOdv079+fyMhIZ5vBgwdTVFTE999/77YvlZWVWK1Wl0dAsNs4UvBPhti/IiN0G21bhPu6RyIiIk1GrReCz5w5k2XLltGqVSvnsVatWvGHP/yBQYMG1XrKa/PmzaSnp3Py5EmaN2/O0qVLSUtLA2D06NGkpqaSmJjIli1bmDp1Kt9++y3Lly8HoKSkhPj4+BrXjI+Pp6SkxNnm0ksvdTmfkJDgPOfubsDp06fzzDPP1Opz+FxBLuTl0NpaxNyIqmOvvA1DXlBBSxERES+oU0XwAwcO1DheWlrqMgrkqc6dO7Np0yby8/P5zW9+w9ixYykoKACM9UwDBw6kW7du3HPPPXz44Yf84x//YOPGjc7Xu5teczgcLsfPbVO9CPx8U3NTp06lvLzc+SgsLKz152pUVVunYD1nCxtrsXG8INc3/RIREWlCah2abr/9du69914+/PBD9u/fz/79+/nwww+57777uOOOO2rdgYiICDp27EifPn2YPn06V155JXPmzHHbtlevXoSHh7Nz504AEhMT3Qa4gwcPOkeTEhMTnaNO1UpLS4GfRpzOFRkZ6byjr/rht+w2yMsB3N0NWHUs7zGjnYiIiNRZrUPT66+/zvDhw/l//+//kZKSQkpKCqNHj2bo0KG8+uqr9e6Qw+GgsrLS7bmtW7dy+vRp2rZtC0B6ejrl5eWsXbvW2WbNmjWUl5eTkZHhbPPll19y6tQpZ5tly5aRlJRUY9ouIO1dVXOEyYUDrD8Y7URERKTOal0RvNqxY8fYtWsXDoeDjh07EhMTU+trTJs2jaFDh5KcnMzRo0dZvHgxM2bMIC8vjw4dOrBo0SKGDRtGXFwcBQUFTJ48maioKNatW0doqLGP2tChQykqKuKNN94AjJIDKSkpzpID5eXldO7cmRtvvJFp06axc+dOxo0bx5NPPunx+iu/rgi++UP46L6Lt7vzbeh+V8P3R0RExE/4vCJ4tZiYGHr06FGvNz9w4ABZWVkUFxdjsVjo0aMHeXl53HzzzRQWFrJixQrmzJlDRUUFycnJDB8+nKeeesoZmMC4u27ixIkMGjQIMDYUnjdvnvO8xWJh+fLlTJgwgT59+tCqVSuys7NrVXvKrzV3P8VY53YiIiLiVp1HmoKJX4802W0wu5ux6NvtuiYTmJNg0mYICXVzXkREpGny9ve3T7dRES8ICTXKCgAOzr0bsOr5kBkKTCIiIvWk0NQUpGXCyIWcaHZOzSpzEoxcqDpNIiIiXlDnNU3iZ9IyeW1fR9Z9+Qm3dwzl7ht+BikZGmESERHxEoWmJmT3jyfJt6cxsFMXSO3g6+6IiIg0KZqea0J2HawAoEOb2pd/EBERkQtTaGoi7HYH3/94DIAOcc193BsREZGmR6GpiSixnuTkaTthISYuaRXl6+6IiIg0OVrT1BTYbfy4ZQWZIasJtSQSZlLpLREREW9TaAp0BbmQl0N3axFzI4ATwOxXjdpNKjUgIiLiNZqeC2QFubBkTM0Ne63FxvGCXN/0S0REpAlSaApUdhvk5eB+65SqY3mPGe1ERESk3hSaAtXeVTVHmFw4wPqD0U5ERETqTaEpUFUc8G47ERERuSCFpkDVPMG77UREROSCFJoCVUqGsSEvpvM0MIG5ndFORERE6k2hKVCFhBplBQBHjeBU9XzIDG3YKyIi4iUKTYEsLRNGLqQ8PM71uDkJRi5UnSYREREvUnHLQJeWya+/bI1p32qmZFjo07WLMSWnESYRERGvUmhqAnYdOskhexoRV10Dl7T0dXdERESaJE3PBTjrydMcqqgEIDUuxse9ERERaboUmgLc94eOAdCmRSQtmoX7uDciIiJNl0JTgNtTFZo0yiQiItKwFJoC3K6DRmjqoNAkIiLSoBSaApndRui+r8gMWcU1Ydu0Oa+IiEgD0t1zgaogF/JyeNhaBBHAN8Cup42Cl6rPJCIi4nUaaQpEBbmwZAwOa5HrcWsxLBljnBcRERGvUmgKNHYb5OXgbvMUcBg/8h7TVJ2IiIiXKTQFmr2r4NwRJhcOsP5gtBMRERGvUWgKNBUHvNtOREREPKLQFGiaJ3i3nYiIiHhEoSnQpGSAOQncrGgymMDczmgnIiIiXqPQFGhCQo2yAoC9xsmqIDVkhtFOREREvEahKRClZcLIhRwk1vW4OQlGLlSdJhERkQag4pYB6kTH4aSfNNE35D/89x3JRMdWTclphElERKRBKDQFqP1lx7ETwtaIHkT3Gezr7oiIiDR5mp4LUPsOHwegfetoH/dEREQkOCg0BSiFJhERkcal0BSgCg+fACBZoUlERKRRKDQFqOqRJoUmERGRxqHQFKAKq0NTqygf90RERCQ46O65AOSwnSGxbB2dQw5z+YlwsN+kUgMiIiINTKEp0BTkYv80h3dDiiAC+Os8+GeSUSVcRS1FREQajKbnAklBLiwZQ0hFketxazEsGWOcFxERkQah0BQo7DbIywEcbrbqdRg/8h4z2omIiIjXKTQFir2rwFp0gQYOsP5gtBMRERGvU2gKFBUHvNtOREREakWhKVA0T/BuOxEREakVn4am1157jR49emA2mzGbzaSnp/PZZ585z1dWVvLQQw8RFxdHTEwMmZmZ7N+/3+Ua+/bt45ZbbiEmJoa4uDgmTpzIqVOnXNqsXLmS3r1706xZMzp06MDrr7/eKJ/Pq1IywJwEblY0GUxgbme0ExEREa/zaWi65JJLmDFjBuvXr2f9+vXceOON3HrrrWzduhWASZMmsXTpUhYvXsxXX31FRUUFI0aMwGYzFjvbbDaGDx/OsWPH+Oqrr1i8eDEfffQRkydPdr7Hnj17GDZsGNdddx3ffPMN06ZNY+LEiXz00Uc++cx1FhIKQ17AAdgd556sClJDZqhek4iISAMxORyOGl/BvtS6dWtefPFF7rrrLtq0acN7773H3XffDUBRURHJycl8+umnDB48mM8++4wRI0ZQWFhIUlISAIsXL2bcuHGUlpZiNpvJyckhNzeXbdu2Od/j/vvv59tvv2X16tUe9clqtWKxWCgvL8dsNnv/Q9fCrpUfEPXPaSSZDv900NzOCEyq0yQiIuLk7e9vv1nTZLPZWLx4MceOHSM9PZ0NGzZw+vRpBg0a5GyTlJREt27dWLXKuENs9erVdOvWzRmYAAYPHkxlZSUbNmxwtjn7GtVt1q9fz+nTp932pbKyEqvV6vLwF980v45rK+fyh7g/wp1vw9iPYdJmBSYREZEG5vPQtHnzZpo3b05kZCT3338/S5cuJS0tjZKSEiIiImjVqpVL+4SEBEpKSgAoKSkhIcF14XOrVq2IiIi4YJuEhATOnDnDoUOH3PZp+vTpWCwW5yM5OdlbH7fedh+swE4IJy/JgO53Qep1mpITERFpBD4PTZ07d2bTpk3k5+fzm9/8hrFjx1JQUHDe9g6HA5Ppp8XQZ//uaZvqGUl3rwWYOnUq5eXlzkdhYWGtPlND2nPoGAAd4pr7uCciIiLBxed7z0VERNCxY0cA+vTpw7p165gzZw533303p06doqyszGW0qbS0lIwM4w6xxMRE1qxZ43K9srIyTp8+7RxdSkxMdI46nX2NsLAwYmNj3fYpMjKSyMhIr31Gb6oOTaltYnzcExERkeDi85GmczkcDiorK+nduzfh4eEsX77cea64uJgtW7Y4Q1N6ejpbtmyhuLjY2WbZsmVERkbSu3dvZ5uzr1Hdpk+fPoSHhzfCJ/Ieu91x1kiTQpOIiEhj8ulI07Rp0xg6dCjJyckcPXqUxYsX88UXX5CXl4fFYuG+++5j8uTJxMbG0rp1a6ZMmUL37t0ZOHAgAIMGDSItLY2srCxefPFFDh8+zJQpUxg/frxzlfz999/PvHnzyM7OZvz48axevZq3336bDz74wJcfvfbsNn7c+jmD7V/xY1gr2pkHXfw1IiIi4jU+DU0HDhwgKyuL4uJiLBYLPXr0IC8vj5tvvhmAl19+mbCwMEaOHMmJEye46aabWLBgAaGhxsLn0NBQPvnkEx544AGuueYaoqKiGDVqFC+99JLzPVJTU/n000955JFH+NOf/kRSUhJz587lzjvv9MlnrpOCXMjLoY21iLkRVcdeeRuGvKC75kRERBqJ39Vp8kc+rdNUkAtLxgDn/jVVLWIfuVDBSURExI0mW6dJ3LDbIC+HmoGJn47lPWa0ExERkQal0OTP9q4Ca9EFGjjA+oPRTkRERBqUQpM/qzjg3XYiIiJSZwpN/qx5wsXb1KadiIiI1JlCkz9LyQBzEs5F3zWYjM16UzIas1ciIiJBSaHJn4WEGmUFAEeN4FT1fMgM7T0nIiLSCBSa/F1aJoxcyLHINq7HzUkqNyAiItKIfL73nHggLZPfb72Evd+sYGz3Zgztd5UxJacRJhERkUajkaYAsfvHk+Tb0ziddiekXqfAJCIi0sgUmgKENuoVERHxLYWmAFB+4jSHKk4BcKlCk4iIiE8oNAWA6lGm+BaRNI/UMjQRERFf0Dewv7PbOPqff5IZsoE4czLYb9B6JhERER9QaPJnBbmQl8N11iKuiwB+BGbPMWo3qdSAiIhIo9L0nL8qyIUlY2pu2GstNo4X5PqmXyIiIkFKockf2W2QlwM43JysOpb3mNFOREREGoVCkz/a4RM0AwAAGnFJREFUu6rmCJMLB1h/MNqJiIhIo1Bo8kcVB7zbTkREROpNockfNU/wbjsRERGpN4Umf5SSYWzIi+k8DUxgbme0ExERkUah0OSPQkKNsgK4WwpeFaSGzFC9JhERkUak0OSv0jJh5ELKw9q4HjcnwciFqtMkIiLSyFTc0p+lZfLrL1tj2reaKRkW+nTtYkzJaYRJRESk0Sk0+bnCskqK7GmE9MiA9q183R0REZGgpek5P1Z5xkax9SQA7VtH+7g3IiIiwU2hyY/9UHYChwOiwkOJjYnwdXdERESCmkKTHyssOwEYo0wm0/nKD4iIiEhjUGjyY/sOHwcgWVNzIiIiPqfQ5Mf2O0NTlI97IiIiIrp7zl/ZbUTuX0VmyG76cgzsV6jUgIiIiA8pNPmjglzIyyHbWgQRwAZg51NGlXAVtRQREfEJTc/5m4JcWDIGrEWux63FxvGCXN/0S0REJMgpNPkTuw3ycnC345zzWN5jRjsRERFpVApN/mTvqpojTC4cYP3BaCciIiKNSqHJn1Qc8G47ERER8RqFJn/SPMG77URERMRrFJr8SUoGmJOA81X/NoG5ndFOREREGpVCkz8JCTXKCgD2GiergtSQGarXJCIi4gMKTf4mLRPHyHcpJdb1uDkJRi5UnSYREREfUXFLP3S4/RAyTobQN+Q/LByZQoSlrTElpxEmERERn1Fo8kN7Dh3DTgiF5t5EXHWjr7sjIiIiaHrOL+0+dAyADm1ifNwTERERqabQ5If2VIWm1DiFJhEREX+h0OSH9hxUaBIREfE3Ck1+aPehCkChSURExJ9oIbg/sduwff81XQ8vp3WIhctir/d1j0RERKSKQpO/KMiFvBxCrUW8HAqEgmPhfxvFLlWbSURExOd8Oj03ffp0fvazn9GiRQvi4+O57bbb2L59u0ubAQMGYDKZXB733HOPS5uysjKysrKwWCxYLBaysrI4cuSIS5vNmzfTv39/oqKiaNeuHc8++ywOh6PBP6NHCnJhyRiwFrkcNlmLjeMFuT7qmIiIiFTzaWhauXIlEyZMID8/n+XLl3PmzBkGDRrEsWPHXNqNHz+e4uJi5+ONN95wOT9q1Cg2bdpEXl4eeXl5bNq0iaysLOd5q9XKzTffTFJSEuvWreOVV17hpZdeYtasWY3yOS/IboO8HMBdgKs6lveY0U5ERER8xqfTc3l5eS7P33nnHeLj49mwYQPXX//Tep7o6GgSExPdXmPbtm3k5eWRn5/P1VdfDcD8+fNJT09n+/btdO7cmUWLFnHy5EkWLFhAZGQk3bp1Y8eOHcyaNYvs7GxMpvNtkNsI9q6qMcLkygHWH4x2qdc1WrdERETElV/dPVdeXg5A69atXY4vWrSIuLg4unbtypQpUzh69Kjz3OrVq7FYLM7ABNCvXz8sFgurVq1ytunfvz+RkZHONoMHD6aoqIjvv/++Rj8qKyuxWq0ujwZTccC77URERKRB+M1CcIfDQXZ2Ntdeey3dunVzHh89ejSpqakkJiayZcsWpk6dyrfffsvy5csBKCkpIT4+vsb14uPjKSkpcba59NJLXc4nJCQ4z6Wmprqcmz59Os8884w3P975NU/wbjsRERFpEH4Tmh588EH+/e9/89VXX7kcHz9+vPP3bt260alTJ/r06cPGjRvp1asXgNvpNYfD4XL83DbVi8DdvXbq1KlkZ2c7n1utVpKTk+vwqTyQkgHmJLAW435dk8k4n5LRMO8vIiIiHvGL6bmHHnqI3NxcPv/8cy655JILtu3Vqxfh4eH/v737D4rqOvsA/l1wd0VcthCEZQWBOBGDoEZIFdsEYvryIyp2ktaQOkanja2NREzIVNNMi3XShE477Zu3U7VjjRObtLEtJLWjY4WKkBbUFLQiGIvhV6IgamHBGGWB5/1j5dbrglwj7F3g+5lhRs89u5yHh53zcO6956Kurg4AYLPZcOGC+6mrixcvKqtJNptNWXXq19bWBuC/K043M5vNCAgIUH2NGB9f17YCAAS3FnA3/p+e7+pHREREutG1aBIRZGdno7CwEIcOHXI7TTaQmpoaOJ1OhIWFAQCSkpLgcDhw7Ngxpc/Ro0fhcDiwcOFCpU9ZWRm6u7uVPgcPHoTdbnc7baeL2Exg+W5c87ulgAuwA8t3c58mIiIiL2AQHTcrevbZZ/G73/0Of/7znxETE6O0W61W+Pn54aOPPsLbb7+Nxx57DMHBwaitrUVubi78/PzwwQcfwNfXtfqSkZGB8+fPK1sRfPvb30ZkZCT+8pe/AHBdYB4TE4NFixbh+9//Purq6rB69Wr88Ic/RG5u7pDj7OzshNVqhcPhGNFVp20lZ1BatBePRRnw9P/Md52S4woTERHR5zLc87euK03btm2Dw+FASkoKwsLClK89e/YAAEwmE/72t78hLS0NMTExWL9+PVJTU1FcXKwUTIDr7rr4+HikpqYiNTUVs2fPxm9/+1vluNVqRVFRET755BMkJibi2WefxQsvvKC6bskb1F+6hiN9seiYvsy1vQALJiIiIq+h64XgQy1yRUREoLS0dMj3CQoKwltvvXXbPvHx8SgrK7uj8XlawyXXpp58UC8REZH38YoLwcmlnkUTERGR12LR5CU6rnbjP5+6LlRn0UREROR9vGafpnGtrxcXq4uR6VOOnkkh8Dem6z0iIiIiugWLJr3V7gUObMR9nefxfyYAPQD+d7tr7yZuNUBEROQ1eHpOT7V7gT887f7A3s4WV3vtXn3GRURERG5YNOmlrxc4sBEDPzrlRtuBTa5+REREpDsWTXppKndfYVIRoPOcqx8RERHpjkWTXq64Py/vrvoRERHRiGLRpJfJ7g8Kvqt+RERENKJYNOklcqHrgbwwDNLBAARMdfUjIiIi3bFo0ouPr2tbAQDiVjjd+H96Pp8/R0RE5CVYNOkpNhNYvhtXTFPU7QF2YPlu7tNERETkRbi5pd5iM/HyCTvaTpVgzdxJePTB2a5TclxhIiIi8ipcafICjf+5hiN9seiZ9QQQ/RALJiIiIi/EoskLNF2+CgCIuocP6iUiIvJWLJp01nG1G47PnACAaUGTdB4NERERDYZFk84ab6wyhQaY4WfiaTkiIiJvxaJJZ02XPwUARPLUHBERkVdj0aSnvl44z5Yh06cci8xn+HBeIiIiL2YQEdF7EN6us7MTVqsVDocDAQEBw/OmtXuBAxvVD+0NsLs2vOT+TERERHdtuOdvrjTpoXYv8Ien1QUTAHS2uNpr9+ozLiIiIhoUiyZP6+t1rTBhoAW+G20HNvFUHRERkZdh0eRpTeXuK0wqAnSec/UjIiIir8GiydOuXBjefkREROQRLJo8bXLo8PYjIiIij2DR5GmRC113ycEwSAcDEDDV1Y+IiIi8BosmT/PxdW0rAMC9cLrx//R8PrSXiIjIy7Bo0kNsJrB8NxAQpm4PsLvauU8TERGR15mg9wDGrdhMYOZi111yVy64rmGKXMgVJiIiIi/FoklPPr5A9EN6j4KIiIg04Ok5IiIiIg1YNBERERFpwKKJiIiISAMWTUREREQasGgiIiIi0oBFExEREZEGLJqIiIiINGDRRERERKQBiyYiIiIiDbgjuAYiAgDo7OzUeSRERESkVf+83T+P3y0WTRp0dXUBACIiInQeCREREd2prq4uWK3Wu34fgwxX+TWG9fX14fz587BYLDAYDMP63p2dnYiIiMDHH3+MgICAYX1vbzIe4hwPMQLjI87xECPAOMeS8RAjcOdxigi6urpgt9vh43P3VyRxpUkDHx8fhIeHj+j3CAgIGNO/6P3GQ5zjIUZgfMQ5HmIEGOdYMh5iBO4szuFYYerHC8GJiIiINGDRRERERKSB7+bNmzfrPYjxztfXFykpKZgwYWyfLR0PcY6HGIHxEed4iBFgnGPJeIgR0DdOXghOREREpAFPzxERERFpwKKJiIiISAMWTUREREQasGgiIiIi0oBFk462bt2K6OhoTJw4EQkJCXj//ff1HtKgXnvtNTz44IOwWCwICQnBV7/6VZw5c0bVJyUlBQaDQfWVlZWl6tPe3o6VK1fCarXCarVi5cqV6OjoUPWprq5GcnIy/Pz8MHXqVGzZsmXYnhs0lM2bN7vFYLPZlOMigs2bN8Nut8PPzw8pKSmoqakZVTFGRUW5xWgwGLBu3ToAozePZWVlWLp0Kex2OwwGA9577z3VcU/mrqCgALGxsTCbzYiNjcW7777rkTidTic2btyI+Ph4+Pv7w2634+mnn8b58+dV7zHQ78CmTZtUfZqbm7F06VL4+/sjODgY69evR3d3t6pPaWkpEhISMHHiRNx7773Yvn37iMcIAKtXr3Yb/4IFC1R9rl+/jueeew7BwcHw9/dHZmYmPvnkE6+JUUucA31ODQYDfvrTnyp9vD2XWuYOT+bqruddIV288847YjQaZceOHVJbWys5OTni7+8vTU1Neg9tQGlpabJr1y45deqUnDhxQhYvXizTpk2TK1euKH2Sk5NlzZo10tLSonx1dHSo3ic9PV3i4uKkvLxcysvLJS4uTpYsWaIcdzgcEhoaKllZWVJdXS0FBQVisVjkZz/7mUfizMvLk1mzZqliaGtrU47n5+eLxWKRgoICqa6ulieffFLCwsKks7Nz1MTY1tamiq+oqEgASElJiYiM3jzu379fXn75ZSkoKBAA8u6776qOeyp35eXl4uvrK6+++qqcPn1aXn31VZkwYYIcOXJkxOPs6OiQr3zlK7Jnzx758MMPpaKiQubPny8JCQmq94iMjJQtW7aoctzV1aUc7+npkbi4OHnkkUekqqpKioqKxG63S3Z2ttKnvr5eJk2aJDk5OVJbWys7duwQo9Eof/rTn0Y0RhGRVatWSXp6umr8ly9fVvVZu3atTJ06VYqKiqSqqkoeeeQRmTNnjvT09HhFjFrivDm+lpYWeeONN8RgMMhHH32k9PH2XGqZOzyVq+GYd1k06eSLX/yirF27VtU2c+ZM2bRpk04jujNtbW0CQEpLS5W25ORkycnJGfQ1tbW1AkA1eVRUVAgA+fDDD0VEZOvWrWK1WuXatWtKn9dee03sdrv09fWNQCRqeXl5MmfOnAGP9fX1ic1mk/z8fKXt2rVrYrVaZfv27SIyOmK8VU5OjkyfPl353mMhj7dOQJ7M3fLlyyU9PV01nrS0NMnKyhrxOAdy7NgxAaCaGCIjI+UXv/jFoK/Zv3+/+Pj4yLlz55S23//+92I2m8XhcIiIyPe+9z2ZOXOm6nXf+c53ZMGCBZ8nlEENVjQtW7Zs0Nd0dHSI0WiUd955R2k7d+6c+Pj4yIEDB0TEu2IU0ZbLZcuWyaJFi1RtoymXIu5zhydzNRzzLk/P6aC7uxuVlZVITU1VtaempqK8vFynUd0Zh8MBAAgKClK1v/322wgODsasWbPw4osvoqurSzlWUVEBq9WK+fPnK20LFiyA1WpV4q6oqEBycjLMZrPSJy0tDefPn0djY+MIRvRfdXV1sNvtiI6ORlZWFurr6wEADQ0NaG1tVeXNbDYjOTlZNf7REGO/7u5uvPXWW/jmN7+pehj1WMjjzTyZu4qKCrfPdlpamm6fbYfDAYPBgC984Quq9p/85Ce45557MHfuXPz4xz9WneqoqKhAXFwc7Ha70paWlobr16+jsrJS6TNQnP/85z/hdDpHMCKXw4cPIyQkBDNmzMCaNWvQ1tamHKusrITT6VSNz263Iy4uTpVLb4/xZhcuXMC+ffvwrW99y+3YaMrlrXOHp3I1XPPu2N421EtdunQJvb29CA0NVbWHhoaitbVVp1FpJyJ44YUX8OUvfxlxcXFK+4oVKxAdHQ2bzYZTp07hpZdewr/+9S8UFRUBAFpbWxESEuL2fiEhIUrcra2tiIqKUh3v/zm1trYiOjp6hKJymT9/Pnbv3o0ZM2bgwoULeOWVV7Bw4ULU1NQoYxwob01NTcoYvT3Gm7333nvo6OjA6tWrlbaxkMdbeTJ3ra2tXvPZvnbtGjZt2oRvfOMbqoeb5uTkYN68eQgMDMSxY8fw0ksvoaGhAb/5zW8AYMAYAgMDYTKZVD+LgeLs6enBpUuXEBYWNmJxZWRk4Otf/zoiIyPR0NCAH/zgB1i0aBEqKythNpvR2toKk8mEwMBAt/HdbvzeFOOt3nzzTVgsFjz++OOq9tGUy4HmDk/lSkSGZd5l0aSjm/+yB1y/ULe2eaPs7GycPHkSf//731Xta9asUf4dFxeH++67D4mJiaiqqsK8efMAuMcMuMc90M9lsNcOt4yMDOXf8fHxSEpKwvTp0/Hmm28qF5oOlTdvj/FmO3fuREZGhuovuLGQx8F4Knfe8Nl2Op3IyspCX18ftm7dqjr2/PPPK/+ePXs2AgMD8bWvfU1ZsQC8O8dPPvmk8u+4uDgkJiYiMjIS+/btcysqbh3faP2svvHGG1ixYgUmTpyoah9NuRxs7hjIcOdqsHju9LPJ03M6CA4Ohq+vr1t129bW5lYFe5vnnnsOe/fuRUlJCcLDw2/bd968eTAajairqwMA2Gw2XLhwwa3fxYsXlbhtNtuAPxfAfZXAE/z9/REfH4+6ujrlLrrb5W00xdjU1ITi4mI888wzt+03FvLoydwN1seTcTudTixfvhwNDQ0oKipSrTINpP8PgrNnzwIYOIb29nY4nc4h45wwYYIyWXtKWFgYIiMjVb+j3d3daG9vdxvf7cbvrTG+//77OHPmzJCfVcB7cznY3OGpXA3XvMuiSQcmkwkJCQnK6Y5+RUVFWLhwoU6juj0RQXZ2NgoLC3Ho0CFNp1dqamrgdDqVpd2kpCQ4HA4cO3ZM6XP06FE4HA4l7qSkJJSVlanOyR88eBB2u93ttIgnXL9+HadPn0ZYWJhyyurmvHV3d6O0tFQ1/tES465duxASEoLFixfftt9YyKMnc5eUlOT22T548KDHPtv9BVNdXR2Ki4s1TXrHjx8HAFWOT506hZaWFqXPwYMHYTabkZCQoPQZKM7ExEQYjcbhCkeTy5cv4+OPP1bGn5CQAKPRqBpfS0sLTp06pcrlaIlx586dSEhIwJw5c4bs6225HGru8FSuhm3e1XzJOA2r/lsfd+7cKbW1tbJhwwbx9/eXxsZGvYc2oO9+97titVrl8OHDqltbr169KiIiZ8+elR/96EfywQcfSENDg+zbt09mzpwpDzzwgHLbqIjrlu7Zs2dLRUWFVFRUSHx8vOqW7o6ODgkNDZWnnnpKqqurpbCwUAICAjx2O35ubq4cPnxY6uvr5ciRI7JkyRKxWCxKXvLz88VqtUphYaFUV1fLU089NeBt694co4hIb2+vTJs2TTZu3KhqH8157OrqkuPHj8vx48cFgPz85z+X48ePK3eNeSp3//jHP8TX11fy8/Pl9OnTkp+fP6xbDtwuTqfTKZmZmRIeHi4nTpxQfVavX78uIq4tEfpfU19fL3v27BG73S6ZmZnK9+i/xfvRRx+VqqoqKS4ulvDw8AFv8X7++eeltrZWdu7cOWy3qd8uxq6uLsnNzZXy8nJpaGiQkpISSUpKkqlTp6pyuXbtWgkPD5fi4mKpqqqSRYsWDXgbu14xDhVnP4fDIZMmTZJt27a5vX405HKouUPEc7kajnmXRZOOfvWrX0lkZKSYTCaZN2+e6vZ9bwNgwK9du3aJiEhzc7M8/PDDEhQUJCaTSaZPny7r16932zvl8uXLsmLFCrFYLGKxWGTFihXS3t6u6nPy5El56KGHxGw2i81mk82bN3vsVvz+vXuMRqPY7XZ5/PHHpaamRjne19cneXl5YrPZxGw2y8MPPyzV1dWjKkYRkb/+9a8CQM6cOaNqH815LCkpGfB3dNWqVSLi2dz98Y9/lJiYGDEajTJz5kwpKCjwSJwNDQ2Dflb79+GqrKyU+fPni9VqlYkTJ0pMTIzk5eXJp59+qvo+TU1NsnjxYvHz85OgoCDJzs5WbbUgInL48GF54IEHxGQySVRU1IAT+3DHePXqVUlNTZUpU6aI0WiUadOmyapVq6S5uVn1Hp999plkZ2dLUFCQ+Pn5yZIlS9z66BnjUHH2+/Wvfy1+fn5ue6WJjI5cDjV3iHg2V3c77xpuBEVEREREt8FrmoiIiIg0YNFEREREpAGLJiIiIiINWDQRERERacCiiYiIiEgDFk1EREREGrBoIiIiItKARRMRERGRBiyaiGhcioqKgsFggMFgQEdHx5D9Gxsblf5z5871wAiJyNuwaCKiMSUlJQUbNmzQ1HfLli1oaWmB1Wodsm9ERARaWlqQm5t7t0MkolFqgt4DICLSi8Vigc1m09TX19cXNpsNkydPHuFREZG34koTEY0Zq1evRmlpKV5//XXlVFpjY6Pm1zc1NWHp0qUIDAyEv78/Zs2ahf3794/cgIloVOFKExGNGa+//jr+/e9/Iy4uDlu2bAEATJkyRfPr161bh+7ubpSVlcHf3x+1tbVcWSIiBYsmIhozrFYrTCYTJk2apPm0282am5vxxBNPID4+HgBw7733DvcQiWgU4+k5IqIb1q9fj1deeQVf+tKXkJeXh5MnT+o9JCLyIiyaiIhueOaZZ1BfX4+VK1eiuroaiYmJ+OUvf6n3sIjIS7BoIqIxxWQyobe393O/PiIiAmvXrkVhYSFyc3OxY8eOYRwdEY1mvKaJiMaUqKgoHD16FI2NjZg8eTKCgoLg46Pt78MNGzYgIyMDM2bMQHt7Ow4dOoT7779/hEdMRKMFV5qIaEx58cUX4evri9jYWEyZMgXNzc2aX9vb24t169bh/vvvR3p6OmJiYrB169YRHC0RjSZcaSKiMWXGjBmoqKj4XK/l9UtEdDsGERG9B0FE5GlRUVFoaWmB0WjEuXPnhnyUSnNzM2JjY9Hd3Y3Y2FicOHHCQyMlIm/BoomIxqWmpiY4nU4Arv2YhrruqaenR9ld3Gw2IyIiYqSHSERehkUTERERkQa8EJyIiIhIAxZNRERERBqwaCIiIiLSgEUTERERkQYsmoiIiIg0YNFEREREpAGLJiIiIiINWDQRERERafD/J8pXe5XN7vYAAAAASUVORK5CYII=",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1.1439755442283902e-5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_init = best_candidate(res)\n",
    "vis_error(x_init, exp_data1, core_props, fluids, core_flood)\n",
    "error_calc(x_init, exp_data1, core_props, fluids, core_flood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using JuMP\n",
    "I'm going to try and use [JuMP](https://github.com/JuliaOpt/JuMP.jl) for history matching of core flooding data. I have used it previously for the history matching of foam flooding, but this is slightly different because there I ony had steady state data and did not need to solve a differential equation. Moreover, I could use automatic differentiation that is not possible to use here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "using JuMP, Ipopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4548076217562965"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weight factors:\n",
    "w_p = ones(length(exp_data1.dp_exp))\n",
    "temp_val, ind_max = findmax(exp_data1.dp_exp)\n",
    "# println(ind_max)\n",
    "w_p[ind_max-1:ind_max+1] .= 10\n",
    "w_p[end-10:end] .= 1\n",
    "w_p[1]=1\n",
    "w_R = ones(length(exp_data1.R_exp))\n",
    "w_R[20:25] .= 1\n",
    "w_R[end-10:end] .= 10\n",
    "\n",
    "# [krw0, kro0, nw, no, swc, sor]\n",
    "\n",
    "function my_f(krw0, kro0, nw, no, swc, sor)\n",
    "    f_val = error_calc([krw0, kro0, nw, no, swc, sor], exp_data1, core_props, \n",
    "            fluids, core_flood, w_p = w_p, w_R = w_R)\n",
    "    return f_val\n",
    "end\n",
    "\n",
    "function ∇f(g_val, krw0, kro0, nw, no, swc, sor)\n",
    "    eps1 = 1e-4\n",
    "    x = [krw0, kro0, nw, no, swc, sor]\n",
    "    f_val = my_f(krw0, kro0, nw, no, swc, sor)\n",
    "    g_val = zeros(length(x))\n",
    "    for j in eachindex(x)\n",
    "        x2 = copy(x)\n",
    "        x2[j]+=eps1\n",
    "        f_val2 = my_f(x2...)\n",
    "        g_val[j] = (f_val2-f_val)/eps1\n",
    "    end\n",
    "    return g_val\n",
    "end\n",
    "\n",
    "# test\n",
    "grad_x = zeros(6)\n",
    "my_f(1.0, 0.8, 3, 4, 0.2, 0.2)\n",
    "\n",
    "# ∇f(1.0, 0.8, 2, 2, 0.1, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Ipopt version 3.12.10, running with linear solver mumps.\n",
      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:        0\n",
      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
      "\n",
      "Total number of variables............................:        6\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:        6\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:        0\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  9.3697354e-03 0.00e+00 0.00e+00   0.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  9.3696120e-03 0.00e+00 6.09e-07  -6.1 6.15e-07    -  9.90e-01 1.00e+00f  1\n",
      "   2  1.1392894e-02 0.00e+00 9.54e-02  -2.2 9.54e-02    -  1.00e+00 2.50e-01f  3\n",
      "   3  1.1392894e-02 0.00e+00 3.01e-02  -2.2 3.01e-02    -  1.00e+00 1.42e-14f 47\n",
      "   4  1.1392894e-02 0.00e+00 3.67e-03  -3.4 3.67e-03    -  1.00e+00 5.68e-14f 45\n",
      "   5  1.1392894e-02 0.00e+00 1.11e-04  -5.0 1.11e-04    -  1.00e+00 9.09e-13f 41\n",
      "   6  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 9.09e-13f 41\n",
      "   7  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "   8  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "   9  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  11  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  12  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  13  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  14  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  15  1.1408078e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  16  1.1423256e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  17  1.1438428e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  18  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 42\n",
      "  19  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  21  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  22  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  23  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  24  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  25  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  26  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  27  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  28  1.1408078e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  29  1.1423256e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30  1.1438428e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  31  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 42\n",
      "  32  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  33  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  34  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  35  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  36  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  37  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  38  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  39  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  40  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  41  1.1408078e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  42  1.1423256e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  43  1.1438428e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  44  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 42\n",
      "  45  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  46  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  47  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  48  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  49  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  50  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  51  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  52  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  53  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  54  1.1408078e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  55  1.1423256e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  56  1.1438428e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 1.00e+00w  1\n",
      "  57  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 42\n",
      "  58  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  59  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  60  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  61  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n",
      "  62  1.1392894e-02 0.00e+00 1.17e-04  -5.0 1.17e-04    -  1.00e+00 2.27e-13f 43\n"
     ]
    },
    {
     "ename": "InterruptException",
     "evalue": "InterruptException:",
     "output_type": "error",
     "traceback": [
      "InterruptException:",
      "",
      "Stacktrace:",
      " [1] (::getfield(Main.FractionalFlow, Symbol(\"##45#49\")){Float64,Float64,Float64,Float64})(::Float64) at /home/ali/projects/peteng/analytical/FractionalFlow/FractionalFlow.jl:357",
      " [2] _broadcast_getindex_evalf at ./broadcast.jl:625 [inlined]",
      " [3] _broadcast_getindex at ./broadcast.jl:598 [inlined]",
      " [4] getindex at ./broadcast.jl:558 [inlined]",
      " [5] copyto_nonleaf!(::Array{Float64,1}, ::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},getfield(Main.FractionalFlow, Symbol(\"##45#49\")){Float64,Float64,Float64,Float64},Tuple{Base.Broadcast.Extruded{Array{Float64,1},Tuple{Bool},Tuple{Int64}}}}, ::Base.OneTo{Int64}, ::Int64, ::Int64) at ./broadcast.jl:982",
      " [6] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},getfield(Main.FractionalFlow, Symbol(\"##45#49\")){Float64,Float64,Float64,Float64},Tuple{Array{Float64,1}}}) at ./broadcast.jl:836",
      " [7] materialize at ./broadcast.jl:798 [inlined]",
      " [8] water_flood(::Main.FractionalFlow.CoreProperties, ::Main.FractionalFlow.Fluids, ::Main.FractionalFlow.CoreyRelativePermeability, ::Main.FractionalFlow.CoreFlooding) at /home/ali/projects/peteng/analytical/FractionalFlow/water_flood.jl:86",
      " [9] #error_calc#3(::Array{Float64,1}, ::Array{Float64,1}, ::typeof(error_calc), ::Array{Float64,1}, ::exp_data, ::Main.FractionalFlow.CoreProperties, ::Main.FractionalFlow.Fluids, ::Main.FractionalFlow.CoreFlooding) at ./In[7]:7",
      " [10] (::getfield(Main, Symbol(\"#kw##error_calc\")))(::NamedTuple{(:w_p, :w_R),Tuple{Array{Float64,1},Array{Float64,1}}}, ::typeof(error_calc), ::Array{Float64,1}, ::exp_data, ::Main.FractionalFlow.CoreProperties, ::Main.FractionalFlow.Fluids, ::Main.FractionalFlow.CoreFlooding) at ./none:0",
      " [11] my_f(::Float64, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64) at ./In[33]:15",
      " [12] ∇f(::SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true}, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64) at ./In[33]:28",
      " [13] (::getfield(JuMP, Symbol(\"##97#100\")){typeof(∇f)})(::SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true}, ::SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true}) at /home/ali/.julia/packages/JuMP/MsUSY/src/nlp.jl:1177",
      " [14] eval_objective_gradient(::JuMP._UserFunctionEvaluator, ::SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true}, ::SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true}) at /home/ali/.julia/packages/JuMP/MsUSY/src/nlp.jl:1140",
      " [15] forward_eval(::Array{Float64,1}, ::Array{Float64,1}, ::Array{JuMP._Derivatives.NodeData,1}, ::SparseArrays.SparseMatrixCSC{Bool,Int64}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::JuMP._Derivatives.UserOperatorRegistry) at /home/ali/.julia/packages/JuMP/MsUSY/src/_Derivatives/forward.jl:165",
      " [16] _forward_eval_all(::NLPEvaluator, ::Array{Float64,1}) at /home/ali/.julia/packages/JuMP/MsUSY/src/nlp.jl:503",
      " [17] macro expansion at /home/ali/.julia/packages/JuMP/MsUSY/src/nlp.jl:536 [inlined]",
      " [18] macro expansion at ./util.jl:213 [inlined]",
      " [19] eval_objective(::NLPEvaluator, ::Array{Float64,1}) at /home/ali/.julia/packages/JuMP/MsUSY/src/nlp.jl:534",
      " [20] eval_objective(::Ipopt.Optimizer, ::Array{Float64,1}) at /home/ali/.julia/packages/Ipopt/ruIXY/src/MOI_wrapper.jl:577",
      " [21] (::getfield(Ipopt, Symbol(\"#eval_f_cb#27\")){Ipopt.Optimizer})(::Array{Float64,1}) at /home/ali/.julia/packages/Ipopt/ruIXY/src/MOI_wrapper.jl:810",
      " [22] eval_f_wrapper(::Int32, ::Ptr{Float64}, ::Int32, ::Ptr{Float64}, ::Ptr{Nothing}) at /home/ali/.julia/packages/Ipopt/ruIXY/src/Ipopt.jl:124",
      " [23] solveProblem(::IpoptProblem) at /home/ali/.julia/packages/Ipopt/ruIXY/src/Ipopt.jl:342",
      " [24] optimize!(::Ipopt.Optimizer) at /home/ali/.julia/packages/Ipopt/ruIXY/src/MOI_wrapper.jl:914",
      " [25] optimize!(::MathOptInterface.Bridges.LazyBridgeOptimizer{Ipopt.Optimizer}) at /home/ali/.julia/packages/MathOptInterface/A2UPd/src/Bridges/bridge_optimizer.jl:225",
      " [26] optimize!(::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.AbstractOptimizer,MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}) at /home/ali/.julia/packages/MathOptInterface/A2UPd/src/Utilities/cachingoptimizer.jl:189",
      " [27] #optimize!#78(::Bool, ::Bool, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(JuMP.optimize!), ::Model, ::Nothing) at /home/ali/.julia/packages/JuMP/MsUSY/src/optimizer_interface.jl:141",
      " [28] optimize! at /home/ali/.julia/packages/JuMP/MsUSY/src/optimizer_interface.jl:111 [inlined] (repeats 2 times)",
      " [29] top-level scope at In[34]:13"
     ]
    }
   ],
   "source": [
    "model = Model(with_optimizer(Ipopt.Optimizer))\n",
    "# [krw0, kro0, nw, no, swc, sor]\n",
    "@variable(model, 0.1 <= kro0 <= 1.0, start = x_init[2])\n",
    "@variable(model, 0.1 <= krw0 <= 1.0, start = x_init[1])\n",
    "@variable(model, 0.01 <= sor <= 0.4, start = x_init[6])\n",
    "@variable(model, 0.01 <= swc <= 0.4, start = x_init[5])\n",
    "@variable(model, 1.0 <= no <= 4.0, start = x_init[4])\n",
    "@variable(model, 1.0 <= nw <= 4.0, start = x_init[3])\n",
    "\n",
    "register(model, :my_f, 6, my_f, ∇f)\n",
    "\n",
    "@NLobjective(model, Min, my_f(krw0, kro0, nw, no, swc, sor))\n",
    "\n",
    "# JuMP.register(model::Model, s::Symbol, dimension::Integer, f::Function,\n",
    "#               ∇f::Function, ∇²f::Function)\n",
    "# my_f(x, y) = (x - 1)^2 + (y - 2)^2\n",
    "# function ∇f(g, x, y)\n",
    "#     g[1] = 2 * (x - 1)\n",
    "#     g[2] = 2 * (y - 2)\n",
    "# end\n",
    "JuMP.optimize!(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.007544830494364934"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_init = [value(krw0), value(kro0), value(nw), value(no), value(swc), value(sor)]\n",
    "vis_error(x_init, exp_data1, core_props, fluids, core_flood)\n",
    "error_calc(x_init, exp_data1, core_props, fluids, core_flood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6-element Array{Float64,1}:\n",
       " 0.4412043349923931 \n",
       " 0.9909989358955247 \n",
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